miχPODS: New baseline

TODO

• more intake usage

• why does the stability diagram load multiple times

• hvplot defaults

  • turn off scroll zoom

  • copy over presentation stuff

Catalog display

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%load_ext rich

# MOM6 run catalog
catalog = {
    "baseline": (
        "baseline",
        "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods",
    ),
    "epbl": ("ePBL", "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods"),
    "kpp.lmd.002": (
        "KPP Ri0=0.5",
        "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.002.mixpods",
    ),
    "kpp.lmd.003": (
        "KPP Ri0=0.5, Ric=0.2,",
        "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.003.mixpods",
    ),
    "kpp.lmd.004": (
        "KPP ν0=2.5, Ric=0.2, Ri0=0.5",
        "gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods",
    ),
    "baseline.N150": (
        "baseline N=150",
        "gmom.e23.GJRAv3.TL319_t061_zstar_N150.baseline.mixpods",
    ),
    "kpp.lmd.004.N150": (
        "KPP ν0=2.5, Ric=0.2, Ri0=0.5, N=150",
        "gmom.e23.GJRAv3.TL319_t061_zstar_N150.kpp.lmd.004.mixpods",
    ),
    "new_baseline.hb": (
        "KD=0, KV=0",
        "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb",
    ),
    "new_baseline.kpp.lmd.004": (
        "KPP ν0=2.5, Ric=0.2, Ri0=0.5",
        "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods",
    ),
    "new_baseline.kpp.lmd.005": (
        "KPP ν0=2.5, Ri0=0.5",
        "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods",
    ),
}
catalog

The rich extension is already loaded. To reload it, use:
  %reload_ext rich

{
    'baseline': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods'),
    'epbl': ('ePBL', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.epbl.001.mixpods'),
    'kpp.lmd.002': (
        'KPP Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.002.mixpods'
    ),
    'kpp.lmd.003': (
        'KPP Ri0=0.5, Ric=0.2,',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.003.mixpods'
    ),
    'kpp.lmd.004': (
        'KPP ν0=2.5, Ric=0.2, Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods'
    ),
    'baseline.N150': ('baseline N=150', 'gmom.e23.GJRAv3.TL319_t061_zstar_N150.baseline.mixpods'),
    'kpp.lmd.004.N150': (
        'KPP ν0=2.5, Ric=0.2, Ri0=0.5, N=150',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N150.kpp.lmd.004.mixpods'
    ),
    'new_baseline.hb': ('KD=0, KV=0', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb'),
    'new_baseline.kpp.lmd.004': (
        'KPP ν0=2.5, Ric=0.2, Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods'
    ),
    'new_baseline.kpp.lmd.005': (
        'KPP ν0=2.5, Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods'
    )
}

Setup

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%load_ext autoreload
%load_ext rich
%load_ext watermark

import warnings

import cf_xarray as cfxr
import dask_jobqueue
import distributed
import holoviews as hv
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tqdm
from datatree import DataTree

import xarray as xr

%aimport pump
from pump import mixpods

hv.notebook_extension("bokeh")

cfxr.set_options(warn_on_missing_variables=False)
xr.set_options(keep_attrs=True, display_expand_data=False)

plt.style.use("bmh")
plt.rcParams["figure.dpi"] = 140

mixing_layer_depth_criteria = {
    "boundary_layer_depth": {"name": "KPPhbl|KPP_OBLdepth|ePBL_h_ML"},
}

%watermark -iv

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The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
The rich extension is already loaded. To reload it, use:
  %reload_ext rich
The watermark extension is already loaded. To reload it, use:
  %reload_ext watermark

ipywidgets   : 8.0.6
cf_xarray    : 0.8.0
dask_jobqueue: 0.7.3
holoviews    : 1.15.4
matplotlib   : 3.7.1
distributed  : 2023.3.0
sys          : 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0]
xarray       : 2023.3.0
flox         : 0.6.10
pandas       : 1.5.3
dcpy         : 0.1.dev387+gd06c937
dask         : 2023.3.0
numpy        : 1.23.5
pump         : 1.0+247.g1f1c5e1.dirty
xrft         : 0.0.0
json         : 2.0.9
xgcm         : 0.6.1
tqdm         : 4.65.0

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if "client" in locals():
    client.close()
    del client
if "cluster" in locals():
    cluster.close()

cluster = dask_jobqueue.PBSCluster(
    cores=4,  # The number of cores you want
    memory="23GB",  # Amount of memory
    processes=1,  # How many processes
    queue="casper",  # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)
    local_directory="/local_scratch/pbs.$PBS_JOBID/dask/spill",
    log_directory="/glade/scratch/dcherian/dask/",
    resource_spec="select=1:ncpus=4:mem=23GB",  # Specify resources
    project="ncgd0011",  # Input your project ID here
    walltime="02:00:00",  # Amount of wall time
    interface="ib0",  # Interface to use
)
cluster.adapt(minimum_jobs=1, maximum_jobs=4)
client = distributed.Client(cluster)
client

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Client

Client-396a8bbb-d3d7-11ed-8a43-3cecef1ac722

Connection method: Cluster object	Cluster type: dask_jobqueue.PBSCluster
Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status

Launch dashboard in JupyterLab

Cluster Info

PBSCluster

41b057d1

Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status	Workers: 0
Total threads: 0	Total memory: 0 B

Scheduler Info

Scheduler

Scheduler-4a9a4697-990e-4dc4-a94e-01ef917bc7d3

Comm: tcp://10.12.206.36:42921	Workers: 0
Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/proxy/8787/status	Total threads: 0
Started: Just now	Total memory: 0 B

Workers

Read

Always read TAO

tao_gridded = mixpods.load_tao()

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:255: UserWarning: The specified Dask chunks separate the stored chunks along dimension "depth" starting at index 42. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:255: UserWarning: The specified Dask chunks separate the stored chunks along dimension "time" starting at index 199726. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:255: UserWarning: The specified Dask chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(

Subset Catalog, build tree

catalog_sub = {
    k: catalog[k]
    for k in catalog.keys()
    if k == "kpp.lmd.004" or ("baseline" in k and "150" not in k)
}
display(catalog_sub)

{
    'baseline': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods'),
    'kpp.lmd.004': (
        'KPP ν0=2.5, Ric=0.2, Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods'
    ),
    'new_baseline.hb': ('KD=0, KV=0', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb'),
    'new_baseline.kpp.lmd.004': (
        'KPP ν0=2.5, Ric=0.2, Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods'
    ),
    'new_baseline.kpp.lmd.005': (
        'KPP ν0=2.5, Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods'
    )
}

datasets = {
    "TAO": tao_gridded,
    # "MITgcm": mitgcm,
}

with warnings.catch_warnings():
    warnings.simplefilter("ignore", category=UserWarning)
    warnings.simplefilter("ignore", category=FutureWarning)
    for short_name, (long_name, folder) in tqdm.tqdm(catalog_sub.items()):
        datasets.update(
            {
                short_name: mixpods.load_mom6_sections(folder).assign_attrs(
                    {"title": long_name}
                )
            }
        )

100%|██████████| 5/5 [06:23<00:00, 76.67s/it] 

datasets["les"] = les["0.0.-140.oct.average.month"].to_dataset()

# Offset LES to work with slicing below
datasets["les"]["time"] = datasets["les"]["time"] + pd.Timedelta(days=25 * 365)

tree = DataTree.from_dict(datasets)
tree

<xarray.DatasetView>
Dimensions:  ()
Data variables:
    *empty*

datatree.DataTree

• Groups: (6)
  <xarray.DatasetView>
  Dimensions:             (time: 212302, depth: 61, depthchi: 6)
  Coordinates: (12/19)
      deepest             (time) float64 dask.array<chunksize=(212302,), meta=np.ndarray>
    * depth               (depth) float64 -300.0 -295.0 -290.0 ... -10.0 -5.0 0.0
      eucmax              (time) float64 dask.array<chunksize=(33130,), meta=np.ndarray>
      latitude            float32 0.0
      longitude           float32 -140.0
      mld                 (time) float64 dask.array<chunksize=(212302,), meta=np.ndarray>
      ...                  ...
      oni                 (time) float32 -0.9 -0.9 -0.9 -0.9 ... nan nan nan nan
      en_mask             (time) bool False False False ... False False False
      ln_mask             (time) bool True True True True ... False False False
      warm_mask           (time) bool True True True True ... True True True True
      cool_mask           (time) bool False False False ... False False False
      enso_transition     (time) <U12 'La-Nina warm' ... '____________'
  Data variables: (12/38)
      N2                  (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
      N2T                 (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
      Ri                  (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
      Rig_T               (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
      S                   (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
      S2                  (time, depth) float32 dask.array<chunksize=(33130, 61), meta=np.ndarray>
      ...                  ...
      shred2              (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
      Rig                 (time, depth) float64 dask.array<chunksize=(33130, 61), meta=np.ndarray>
      sst                 (time) float64 dask.array<chunksize=(33130,), meta=np.ndarray>
      Tflx_dia_diff       (time, depthchi) float64 nan nan nan nan ... nan nan nan
      Rif                 (time, depthchi) float64 dask.array<chunksize=(33130, 6), meta=np.ndarray>
      Jb                  (time, depthchi) float64 dask.array<chunksize=(33130, 6), meta=np.ndarray>
  Attributes:
      CREATION_DATE:                23:26 24-FEB-2021
      Data_Source:                  Global Tropical Moored Buoy Array Project O...
      File_info:                    Contact: Dai.C.McClurg@noaa.gov
      Request_for_acknowledgement:  If you use these data in publications or pr...
      _FillValue:                   1.0000000409184788e+35
      array:                        TAO/TRITON
      missing_value:                1.0000000409184788e+35
      platform_code:                0n165e
      site_code:                    0n165e
      wmo_platform_code:            52321

  TAO

  • Groups: (0)
  • Dimensions:
    • time: 212302
    • depth: 61
    • depthchi: 6
  • Coordinates: (19)
    • deepest
      (time)
      float64
      dask.array<chunksize=(212302,), meta=np.ndarray>

      description :

      Deepest depth with a valid observation

      units :

      m

      Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

    • depth
      (depth)
      float64
      -300.0 -295.0 -290.0 ... -5.0 0.0

      axis :

      Z

      positive :

      up

      units :

      m

      array([-300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
             -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
             -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
             -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
             -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
              -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
                0.])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(33130,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 17 graph layers Data type float64 numpy.ndarray

    • latitude
      ()
      float32
      0.0

      array(0., dtype=float32)

    • longitude
      ()
      float32
      -140.0

      array(-140., dtype=float32)

    • mld
      (time)
      float64
      dask.array<chunksize=(212302,), meta=np.ndarray>

      long_name :

      $z_{MLD}$

      units :

      m

      Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(33130,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 20 graph layers Data type float64 numpy.ndarray

    • reference_pressure
      ()
      int64
      0

      array(0)

    • shallowest
      (time)
      float64
      dask.array<chunksize=(212302,), meta=np.ndarray>

      Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

    • time
      (time)
      datetime64[ns]
      1996-01-01 ... 2020-03-20T21:00:00

      array(['1996-01-01T00:00:00.000000000', '1996-01-01T01:00:00.000000000',
             '1996-01-01T02:00:00.000000000', ..., '2020-03-20T19:00:00.000000000',
             '2020-03-20T20:00:00.000000000', '2020-03-20T21:00:00.000000000'],
            dtype='datetime64[ns]')

    • zeuc
      (depth, time)
      float64
      dask.array<chunksize=(42, 132856), meta=np.ndarray>

      Array Chunk Bytes 98.80 MiB 42.57 MiB Shape (61, 212302) (42, 132856) Dask graph 4 chunks in 3 graph layers Data type float64 numpy.ndarray

    • depthchi
      (depthchi)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      positive :

      up

      units :

      m

      array([-89., -69., -59., -49., -39., -29.])

    • dcl_mask
      (depth, time)
      bool
      dask.array<chunksize=(61, 33130), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 12.35 MiB 5.82 MiB Shape (61, 212302) (61, 100000) Dask graph 3 chunks in 45 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      -0.9 -0.9 -0.9 -0.9 ... nan nan nan

      array([-0.9, -0.9, -0.9, ...,  nan,  nan,  nan], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      'La-Nina warm' ... '____________'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['La-Nina warm', 'La-Nina warm', 'La-Nina warm', ...,
             '____________', '____________', '____________'], dtype='<U12')

  • Data variables: (38)
    • N2
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $N^2$

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

    • N2T
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $N^2_T$

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

    • Ri
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $Ri_g$

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

    • Rig_T
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 7 graph layers Data type float64 numpy.ndarray

    • S
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      41

      generic_name :

      sal

      long_name :

      SALINITY (PSU)

      name :

      S

      standard_name :

      sea_water_salinity

      units :

      PSU

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

    • S2
      (time, depth)
      float32
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $S^2$

      Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray

    • T
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      FORTRAN_format :

      f10.2

      epic_code :

      20

      generic_name :

      temp

      long_name :

      TEMPERATURE (C)

      name :

      T

      standard_name :

      sea_water_temperature

      units :

      C

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

    • dens
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $ρ$

      standard_name :

      sea_water_potential_density

      units :

      kg/m3

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

    • densT
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      description :

      density using T, S

      long_name :

      $ρ_T$

      standard_name :

      sea_water_potential_density

      units :

      kg/m3

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

    • lwnet
      (time)
      float32
      dask.array<chunksize=(212302,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1136

      generic_name :

      qln

      long_name :

      NET LONGWAVE RADIATION

      name :

      LWN

      units :

      W m-2

      Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • qlat
      (time)
      float32
      dask.array<chunksize=(212302,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      137

      generic_name :

      qlat

      long_name :

      LATENT HEAT FLUX

      name :

      QL

      units :

      W m-2

      Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • qnet
      (time)
      float32
      dask.array<chunksize=(212302,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      210

      generic_name :

      qtot

      long_name :

      TOTAL HEAT FLUX

      name :

      QT

      units :

      W/M**2

      standard_name :

      surface_downward_heat_flux_in_sea_water

      Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • qsen
      (time)
      float32
      dask.array<chunksize=(212302,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      138

      generic_name :

      qsen

      long_name :

      SENSIBLE HEAT FLUX

      name :

      QS

      units :

      W m-2

      Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • swnet
      (time)
      float32
      dask.array<chunksize=(212302,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1495

      generic_name :

      sw

      long_name :

      NET SHORTWAVE RADIATION

      name :

      SWN

      units :

      W/M**2

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float64
      dask.array<chunksize=(212302,), meta=np.ndarray>

      Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray

    • taux
      (time)
      float64
      dask.array<chunksize=(212302,), meta=np.ndarray>

      standard_name :

      surface_downward_x_stress

      Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

    • tauy
      (time)
      float64
      dask.array<chunksize=(212302,), meta=np.ndarray>

      standard_name :

      surface_downward_y_stress

      Array Chunk Bytes 1.62 MiB 1.62 MiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

    • theta
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      description :

      potential temperature using T, S=35

      long_name :

      $θ$

      standard_name :

      sea_water_potential_temperature

      units :

      degC

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float64 numpy.ndarray

    • u
      (time, depth)
      float32
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1205

      generic_name :

      u

      long_name :

      u

      name :

      u

      standard_name :

      sea_water_x_velocity

      units :

      m/s

      Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray

    • v
      (time, depth)
      float32
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1206

      generic_name :

      v

      long_name :

      v

      name :

      v

      standard_name :

      sea_water_y_velocity

      units :

      m/s

      Array Chunk Bytes 49.40 MiB 23.27 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray

    • wind_dir
      (time)
      float32
      dask.array<chunksize=(212302,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      410

      generic_name :

      long_name :

      WIND DIRECTION

      name :

      WD

      standard_name :

      wind_from_direction

      units :

      degrees

      Array Chunk Bytes 829.30 kiB 829.30 kiB Shape (212302,) (212302,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • pressure
      (depth)
      float64
      301.9 296.8 291.8 ... 5.028 -0.0

      standard_name :

      sea_water_pressure

      units :

      dbar

      array([301.87732362, 296.84242473, 291.80764803, 286.77299352,
             281.73846121, 276.70405112, 271.66976325, 266.63559761,
             261.60155422, 256.56763308, 251.5338342 , 246.5001576 ,
             241.46660329, 236.43317126, 231.39986155, 226.36667414,
             221.33360906, 216.30066632, 211.26784592, 206.23514788,
             201.2025722 , 196.17011889, 191.13778797, 186.10557945,
             181.07349333, 176.04152963, 171.00968835, 165.97796951,
             160.94637311, 155.91489917, 150.8835477 , 145.8523187 ,
             140.82121218, 135.79022817, 130.75936665, 125.72862766,
             120.69801119, 115.66751726, 110.63714587, 105.60689704,
             100.57677078,  95.54676709,  90.51688599,  85.48712749,
              80.4574916 ,  75.42797832,  70.39858766,  65.36931965,
              60.34017428,  55.31115157,  50.28225153,  45.25347416,
              40.22481948,  35.1962875 ,  30.16787822,  25.13959167,
              20.11142784,  15.08338675,  10.0554684 ,   5.02767282,
              -0.        ])

    • SA
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      standard_name :

      sea_water_absolute_salinity

      units :

      g/kg

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 5 graph layers Data type float64 numpy.ndarray

    • CT
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      standard_name :

      sea_water_conservative_temperature

      units :

      degC

      reference_scale :

      ITS-90

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 9 graph layers Data type float64 numpy.ndarray

    • α
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      units :

      1/K

      standard_name :

      sea_water_thermal_expansion_coefficient

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 10 graph layers Data type float64 numpy.ndarray

    • β
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      units :

      kg/g

      standard_name :

      sea_water_haline_contraction_coefficient

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 10 graph layers Data type float64 numpy.ndarray

    • Tz
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      ℃/m

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray

    • Sz
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      g/kg/m

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray

    • chi
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • KT
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      ocean_vertical_heat_diffusivity

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • eps
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • Jq
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • shred2
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 8 graph layers Data type float64 numpy.ndarray

    • Rig
      (time, depth)
      float64
      dask.array<chunksize=(33130, 61), meta=np.ndarray>

      long_name :

      $Ri^g$

      Array Chunk Bytes 98.80 MiB 46.54 MiB Shape (212302, 61) (100000, 61) Dask graph 3 chunks in 7 graph layers Data type float64 numpy.ndarray

    • sst
      (time)
      float64
      dask.array<chunksize=(33130,), meta=np.ndarray>

      description :

      potential temperature using T, S=35

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      units :

      degC

      Array Chunk Bytes 1.62 MiB 781.25 kiB Shape (212302,) (100000,) Dask graph 3 chunks in 4 graph layers Data type float64 numpy.ndarray

    • Tflx_dia_diff
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      ocean_vertical_diffusive_heat_flux

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • Rif
      (time, depthchi)
      float64
      dask.array<chunksize=(33130, 6), meta=np.ndarray>

      standard_name :

      flux_richardson_number

      Array Chunk Bytes 9.72 MiB 4.58 MiB Shape (212302, 6) (100000, 6) Dask graph 3 chunks in 52 graph layers Data type float64 numpy.ndarray

    • Jb
      (time, depthchi)
      float64
      dask.array<chunksize=(33130, 6), meta=np.ndarray>

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      Array Chunk Bytes 9.72 MiB 4.58 MiB Shape (212302, 6) (100000, 6) Dask graph 3 chunks in 49 graph layers Data type float64 numpy.ndarray

  • Attributes: (10)

    CREATION_DATE :

    23:26 24-FEB-2021

    Data_Source :

    Global Tropical Moored Buoy Array Project Office/NOAA/PMEL

    File_info :

    Contact: Dai.C.McClurg@noaa.gov

    Request_for_acknowledgement :

    If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115

    _FillValue :

    1.0000000409184788e+35

    array :

    TAO/TRITON

    missing_value :

    1.0000000409184788e+35

    platform_code :

    0n165e

    site_code :

    0n165e

    wmo_platform_code :

    52321

  <xarray.DatasetView>
  Dimensions:           (time: 533544, zi: 27, zl: 27, nv: 2)
  Coordinates: (12/16)
    * nv                (nv) float64 1.0 2.0
    * time              (time) datetime64[ns] 1958-01-06T00:30:00 ... 2020-12-0...
      xh                float64 -140.0
      yh                float64 0.0625
      yq                float64 -0.0625
    * zi                (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0
      ...                ...
      oni               (time) float32 nan nan nan nan ... -1.175 -1.175 -1.175
      en_mask           (time) bool False False False False ... False False False
      ln_mask           (time) bool False False False False ... True True True
      warm_mask         (time) bool True True True True ... True True True True
      cool_mask         (time) bool False False False False ... False False False
      enso_transition   (time) <U12 '____________' ... 'La-Nina warm'
  Data variables: (12/38)
      KPP_OBLdepth      (time) float32 dask.array<chunksize=(533544,), meta=np.ndarray>
      KPP_buoyFlux      (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_kheat         (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      Kd_heat           (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      Kv_u              (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      Kv_v              (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      ...                ...
      Jq                (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      shear_prod        (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      eps               (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      chi               (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      Rif               (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      sst               (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    baseline

  baseline

  • Groups: (0)
  • Dimensions:
    • time: 533544
    • zi: 27
    • zl: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      cartesian_axis :

      N

      long_name :

      vertex number

      units :

      none

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      1958-01-06T00:30:00 ... 2020-12-...

      array(['1958-01-06T00:30:00.000000000', '1958-01-06T01:30:00.000000000',
             '1958-01-06T02:30:00.000000000', ..., '2020-12-05T21:30:00.000000000',
             '2020-12-05T22:30:00.000000000', '2020-12-05T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      cartesian_axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 4.07 MiB 68.44 kiB Shape (533544,) (8760,) Dask graph 61 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 4.07 MiB 68.44 kiB Shape (533544,) (8760,) Dask graph 61 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 13.74 MiB 230.98 kiB Shape (27, 533544) (27, 8760) Dask graph 61 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      nan nan nan ... -1.175 -1.175

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([       nan,        nan,        nan, ..., -1.1752218, -1.1752218,
             -1.1752218], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (38)
    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(533544,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_kheat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Heat diffusivity due to KPP, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      ocean_vertical_heat_diffusivity

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      ocean_vertical_y_viscosity

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(533544,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      standard_name :

      ocean_vertical_diffusive_heat_flux

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(533544,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(533544,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(533544,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(533544,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 54.95 MiB 0.90 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g$

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(533544,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (533544,) (533544,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 109.91 MiB 1.80 MiB Shape (533544, 27) (8760, 27) Dask graph 61 chunks in 6 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      flux_richardson_number

      Array Chunk Bytes 54.95 MiB 889.69 kiB Shape (533544, 27) (8760, 26) Dask graph 122 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 2.04 MiB 34.22 kiB Shape (533544,) (8760,) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    baseline

  <xarray.DatasetView>
  Dimensions:                         (time: 218424, zi: 27, zl: 27, nv: 2)
  Coordinates: (12/16)
    * nv                              (nv) float64 1.0 2.0
    * time                            (time) datetime64[ns] 2003-01-07T00:30:00...
      xh                              float64 -140.0
      yh                              float64 0.0625
      yq                              float64 -0.0625
    * zi                              (zi) float64 -230.8 -212.0 ... -2.5 -0.0
      ...                              ...
      oni                             (time) float32 nan nan nan ... nan nan nan
      en_mask                         (time) bool False False ... False False
      ln_mask                         (time) bool False False ... False False
      warm_mask                       (time) bool True True True ... True True
      cool_mask                       (time) bool False False ... False False
      enso_transition                 (time) <U12 '____________' ... '_________...
  Data variables: (12/53)
      KPP_N2                          (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLT_temp_budget             (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLtransport_heat            (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_OBLdepth                    (time) float32 dask.array<chunksize=(218424,), meta=np.ndarray>
      KPP_buoyFlux                    (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_kheat                       (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      ...                              ...
      Jq                              (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      shear_prod                      (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      eps                             (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      chi                             (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      Rif                             (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      sst                             (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • time: 218424
    • zi: 27
    • zl: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      cartesian_axis :

      N

      long_name :

      vertex number

      units :

      none

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-07T00:30:00 ... 2028-01-...

      array(['2003-01-07T00:30:00.000000000', '2003-01-07T01:30:00.000000000',
             '2003-01-07T02:30:00.000000000', ..., '2028-01-06T21:30:00.000000000',
             '2028-01-06T22:30:00.000000000', '2028-01-06T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      cartesian_axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.67 MiB 68.44 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.67 MiB 68.44 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 5.62 MiB 230.98 kiB Shape (27, 218424) (27, 8760) Dask graph 25 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      nan nan nan nan ... nan nan nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... '____________'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             '____________', '____________', '____________'], dtype='<U12')

  • Data variables: (53)
    • KPP_N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLT_temp_budget
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat content change due to non-local transport, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_kheat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Heat diffusivity due to KPP, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • S2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of vertical shear used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • T_advection_xy
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Horizontal convergence of residual mean advective fluxes of heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • T_lbdxy_cont_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Lateral diffusion tracer content tendency for T

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      standard_name :

      ocean_vertical_diffusive_heat_flux

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Th_tendency_vert_remap
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Vertical remapping tracer content tendency for Heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • boundary_forcing_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Boundary forcing heat tendency

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • frazil_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat tendency due to frazil formation

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • opottempdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • opottemppmdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • opottemptend
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of Sea Water Potential Temperature Expressed as Heat Content

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 22.50 MiB 0.90 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 44.99 MiB 1.80 MiB Shape (218424, 27) (8760, 27) Dask graph 25 chunks in 6 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 22.50 MiB 889.69 kiB Shape (218424, 27) (8760, 26) Dask graph 50 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 853.22 kiB 34.22 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:               (time: 534360, zl: 27, zi: 27, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 1958-01-01T00:30:00 ... 2018-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 nan nan nan nan nan ... nan nan nan nan
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool False False False ... False False False
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 '____________' ... 'El-Nino warm'
  Data variables: (12/48)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      ...                    ...
      Jq                    (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KD=0, KV=0

  new_baseline.hb

  • Groups: (0)
  • Dimensions:
    • time: 534360
    • zl: 27
    • zi: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      1958-01-01T00:30:00 ... 2018-12-...

      array(['1958-01-01T00:30:00.000000000', '1958-01-01T01:30:00.000000000',
             '1958-01-01T02:30:00.000000000', ..., '2018-12-31T21:30:00.000000000',
             '2018-12-31T22:30:00.000000000', '2018-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 13.76 MiB 230.98 kiB Shape (27, 534360) (27, 8760) Dask graph 61 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      nan nan nan nan ... nan nan nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... 'El-Nino warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')

  • Data variables: (48)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 55.04 MiB 0.90 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 110.07 MiB 1.80 MiB Shape (534360, 27) (8760, 27) Dask graph 61 chunks in 6 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 55.04 MiB 889.69 kiB Shape (534360, 27) (8760, 26) Dask graph 122 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 2.04 MiB 34.22 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KD=0, KV=0

  <xarray.DatasetView>
  Dimensions:               (time: 113880, zl: 27, zi: 27, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2003-01-01T00:30:00 ... 2015-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 nan nan nan nan nan ... nan nan nan nan
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool False False False ... False False False
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 '____________' ... 'El-Nino warm'
  Data variables: (12/48)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      ...                    ...
      Jq                    (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  new_baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • time: 113880
    • zl: 27
    • zi: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2015-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2015-12-31T21:30:00.000000000',
             '2015-12-31T22:30:00.000000000', '2015-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 2.93 MiB 230.98 kiB Shape (27, 113880) (27, 8760) Dask graph 13 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      nan nan nan nan ... nan nan nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... 'El-Nino warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')

  • Data variables: (48)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 23.46 MiB 1.80 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 6 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 444.84 kiB 34.22 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:               (time: 105120, zl: 27, zi: 27, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2003-01-01T00:30:00 ... 2014-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 nan nan nan nan nan ... nan nan nan nan
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool False False False ... False False False
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 '____________' ... '____________'
  Data variables: (12/48)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      ...                    ...
      Jq                    (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ri0=0.5

  new_baseline.kpp.lmd.005

  • Groups: (0)
  • Dimensions:
    • time: 105120
    • zl: 27
    • zi: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2014-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2014-12-31T21:30:00.000000000',
             '2014-12-31T22:30:00.000000000', '2014-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 821.25 kiB 68.44 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 821.25 kiB 68.44 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 2.71 MiB 230.98 kiB Shape (27, 105120) (27, 8760) Dask graph 12 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      nan nan nan nan ... nan nan nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... '____________'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             '____________', '____________', '____________'], dtype='<U12')

  • Data variables: (48)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 21.65 MiB 1.80 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 6 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 410.62 kiB 34.22 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ri0=0.5

• Dimensions:
• Coordinates: (0)
• Data variables: (0)
• Attributes: (0)

Modify catalog selection?

if "les" in tree:
    tree["les"] = tree["les"].isel(z=slice(-2))
    tree["les"]["KT"].attrs["standard_name"] = "ocean_vertical_heat_diffusivity"

if "micro" in locals():
    tree.update(micro)

Post-process catalog subset

tree = tree.sel(time=slice("2000", "2017"))
tree

<xarray.DatasetView>
Dimensions:  ()
Data variables:
    *empty*

datatree.DataTree

• Groups: (6)
  <xarray.DatasetView>
  Dimensions:             (time: 157800, depth: 61, depthchi: 6)
  Coordinates: (12/19)
      deepest             (time) float64 dask.array<chunksize=(157800,), meta=np.ndarray>
    * depth               (depth) float64 -300.0 -295.0 -290.0 ... -10.0 -5.0 0.0
      eucmax              (time) float64 dask.array<chunksize=(98066,), meta=np.ndarray>
      latitude            float32 0.0
      longitude           float32 -140.0
      mld                 (time) float64 dask.array<chunksize=(157800,), meta=np.ndarray>
      ...                  ...
      oni                 (time) float32 -1.66 -1.66 -1.66 ... -0.87 -0.87 -0.87
      en_mask             (time) bool False False False ... False False False
      ln_mask             (time) bool True True True True ... True True True True
      warm_mask           (time) bool False False False False ... True True True
      cool_mask           (time) bool True True True True ... False False False
      enso_transition     (time) <U12 'La-Nina cool' ... 'La-Nina warm'
  Data variables: (12/38)
      N2                  (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray>
      N2T                 (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray>
      Ri                  (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray>
      Rig_T               (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray>
      S                   (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray>
      S2                  (time, depth) float32 dask.array<chunksize=(98066, 61), meta=np.ndarray>
      ...                  ...
      shred2              (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray>
      Rig                 (time, depth) float64 dask.array<chunksize=(98066, 61), meta=np.ndarray>
      sst                 (time) float64 dask.array<chunksize=(98066,), meta=np.ndarray>
      Tflx_dia_diff       (time, depthchi) float64 nan nan nan nan ... nan nan nan
      Rif                 (time, depthchi) float64 dask.array<chunksize=(98066, 6), meta=np.ndarray>
      Jb                  (time, depthchi) float64 dask.array<chunksize=(98066, 6), meta=np.ndarray>
  Attributes:
      CREATION_DATE:                23:26 24-FEB-2021
      Data_Source:                  Global Tropical Moored Buoy Array Project O...
      File_info:                    Contact: Dai.C.McClurg@noaa.gov
      Request_for_acknowledgement:  If you use these data in publications or pr...
      _FillValue:                   1.0000000409184788e+35
      array:                        TAO/TRITON
      missing_value:                1.0000000409184788e+35
      platform_code:                0n165e
      site_code:                    0n165e
      wmo_platform_code:            52321

  TAO

  • Groups: (0)
  • Dimensions:
    • time: 157800
    • depth: 61
    • depthchi: 6
  • Coordinates: (19)
    • deepest
      (time)
      float64
      dask.array<chunksize=(157800,), meta=np.ndarray>

      description :

      Deepest depth with a valid observation

      units :

      m

      Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • depth
      (depth)
      float64
      -300.0 -295.0 -290.0 ... -5.0 0.0

      axis :

      Z

      positive :

      up

      units :

      m

      array([-300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
             -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
             -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
             -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
             -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
              -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
                0.])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(98066,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.20 MiB 766.14 kiB Shape (157800,) (98066,) Dask graph 2 chunks in 18 graph layers Data type float64 numpy.ndarray

    • latitude
      ()
      float32
      0.0

      array(0., dtype=float32)

    • longitude
      ()
      float32
      -140.0

      array(-140., dtype=float32)

    • mld
      (time)
      float64
      dask.array<chunksize=(157800,), meta=np.ndarray>

      long_name :

      $z_{MLD}$

      units :

      m

      Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(98066,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.20 MiB 766.14 kiB Shape (157800,) (98066,) Dask graph 2 chunks in 21 graph layers Data type float64 numpy.ndarray

    • reference_pressure
      ()
      int64
      0

      array(0)

    • shallowest
      (time)
      float64
      dask.array<chunksize=(157800,), meta=np.ndarray>

      Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • time
      (time)
      datetime64[ns]
      2000-01-01 ... 2017-12-31T23:00:00

      array(['2000-01-01T00:00:00.000000000', '2000-01-01T01:00:00.000000000',
             '2000-01-01T02:00:00.000000000', ..., '2017-12-31T21:00:00.000000000',
             '2017-12-31T22:00:00.000000000', '2017-12-31T23:00:00.000000000'],
            dtype='datetime64[ns]')

    • zeuc
      (depth, time)
      float64
      dask.array<chunksize=(42, 97792), meta=np.ndarray>

      Array Chunk Bytes 73.44 MiB 31.34 MiB Shape (61, 157800) (42, 97792) Dask graph 4 chunks in 4 graph layers Data type float64 numpy.ndarray

    • depthchi
      (depthchi)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      positive :

      up

      units :

      m

      array([-89., -69., -59., -49., -39., -29.])

    • dcl_mask
      (depth, time)
      bool
      dask.array<chunksize=(61, 98066), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 9.18 MiB 5.70 MiB Shape (61, 157800) (61, 98066) Dask graph 2 chunks in 46 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      -1.66 -1.66 -1.66 ... -0.87 -0.87

      array([-1.66, -1.66, -1.66, ..., -0.87, -0.87, -0.87], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      'La-Nina cool' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (38)
    • N2
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $N^2$

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • N2T
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $N^2_T$

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • Ri
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $Ri_g$

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • Rig_T
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray

    • S
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      41

      generic_name :

      sal

      long_name :

      SALINITY (PSU)

      name :

      S

      standard_name :

      sea_water_salinity

      units :

      PSU

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • S2
      (time, depth)
      float32
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $S^2$

      Array Chunk Bytes 36.72 MiB 22.82 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray

    • T
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      FORTRAN_format :

      f10.2

      epic_code :

      20

      generic_name :

      temp

      long_name :

      TEMPERATURE (C)

      name :

      T

      standard_name :

      sea_water_temperature

      units :

      C

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • dens
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $ρ$

      standard_name :

      sea_water_potential_density

      units :

      kg/m3

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • densT
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      description :

      density using T, S

      long_name :

      $ρ_T$

      standard_name :

      sea_water_potential_density

      units :

      kg/m3

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • lwnet
      (time)
      float32
      dask.array<chunksize=(157800,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1136

      generic_name :

      qln

      long_name :

      NET LONGWAVE RADIATION

      name :

      LWN

      units :

      W m-2

      Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • qlat
      (time)
      float32
      dask.array<chunksize=(157800,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      137

      generic_name :

      qlat

      long_name :

      LATENT HEAT FLUX

      name :

      QL

      units :

      W m-2

      Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • qnet
      (time)
      float32
      dask.array<chunksize=(157800,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      210

      generic_name :

      qtot

      long_name :

      TOTAL HEAT FLUX

      name :

      QT

      units :

      W/M**2

      standard_name :

      surface_downward_heat_flux_in_sea_water

      Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • qsen
      (time)
      float32
      dask.array<chunksize=(157800,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      138

      generic_name :

      qsen

      long_name :

      SENSIBLE HEAT FLUX

      name :

      QS

      units :

      W m-2

      Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • swnet
      (time)
      float32
      dask.array<chunksize=(157800,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1495

      generic_name :

      sw

      long_name :

      NET SHORTWAVE RADIATION

      name :

      SWN

      units :

      W/M**2

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float64
      dask.array<chunksize=(157800,), meta=np.ndarray>

      Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

    • taux
      (time)
      float64
      dask.array<chunksize=(157800,), meta=np.ndarray>

      standard_name :

      surface_downward_x_stress

      Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • tauy
      (time)
      float64
      dask.array<chunksize=(157800,), meta=np.ndarray>

      standard_name :

      surface_downward_y_stress

      Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • theta
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      description :

      potential temperature using T, S=35

      long_name :

      $θ$

      standard_name :

      sea_water_potential_temperature

      units :

      degC

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • u
      (time, depth)
      float32
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1205

      generic_name :

      u

      long_name :

      u

      name :

      u

      standard_name :

      sea_water_x_velocity

      units :

      m/s

      Array Chunk Bytes 36.72 MiB 22.82 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray

    • v
      (time, depth)
      float32
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1206

      generic_name :

      v

      long_name :

      v

      name :

      v

      standard_name :

      sea_water_y_velocity

      units :

      m/s

      Array Chunk Bytes 36.72 MiB 22.82 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray

    • wind_dir
      (time)
      float32
      dask.array<chunksize=(157800,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      410

      generic_name :

      long_name :

      WIND DIRECTION

      name :

      WD

      standard_name :

      wind_from_direction

      units :

      degrees

      Array Chunk Bytes 616.41 kiB 616.41 kiB Shape (157800,) (157800,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • pressure
      (depth)
      float64
      301.9 296.8 291.8 ... 5.028 -0.0

      standard_name :

      sea_water_pressure

      units :

      dbar

      array([301.87732362, 296.84242473, 291.80764803, 286.77299352,
             281.73846121, 276.70405112, 271.66976325, 266.63559761,
             261.60155422, 256.56763308, 251.5338342 , 246.5001576 ,
             241.46660329, 236.43317126, 231.39986155, 226.36667414,
             221.33360906, 216.30066632, 211.26784592, 206.23514788,
             201.2025722 , 196.17011889, 191.13778797, 186.10557945,
             181.07349333, 176.04152963, 171.00968835, 165.97796951,
             160.94637311, 155.91489917, 150.8835477 , 145.8523187 ,
             140.82121218, 135.79022817, 130.75936665, 125.72862766,
             120.69801119, 115.66751726, 110.63714587, 105.60689704,
             100.57677078,  95.54676709,  90.51688599,  85.48712749,
              80.4574916 ,  75.42797832,  70.39858766,  65.36931965,
              60.34017428,  55.31115157,  50.28225153,  45.25347416,
              40.22481948,  35.1962875 ,  30.16787822,  25.13959167,
              20.11142784,  15.08338675,  10.0554684 ,   5.02767282,
              -0.        ])

    • SA
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      standard_name :

      sea_water_absolute_salinity

      units :

      g/kg

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 6 graph layers Data type float64 numpy.ndarray

    • CT
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      standard_name :

      sea_water_conservative_temperature

      units :

      degC

      reference_scale :

      ITS-90

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 10 graph layers Data type float64 numpy.ndarray

    • α
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      units :

      1/K

      standard_name :

      sea_water_thermal_expansion_coefficient

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray

    • β
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      units :

      kg/g

      standard_name :

      sea_water_haline_contraction_coefficient

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray

    • Tz
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      ℃/m

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray

    • Sz
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      g/kg/m

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray

    • chi
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • KT
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      ocean_vertical_heat_diffusivity

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • eps
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • Jq
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • shred2
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray

    • Rig
      (time, depth)
      float64
      dask.array<chunksize=(98066, 61), meta=np.ndarray>

      long_name :

      $Ri^g$

      Array Chunk Bytes 73.44 MiB 45.64 MiB Shape (157800, 61) (98066, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray

    • sst
      (time)
      float64
      dask.array<chunksize=(98066,), meta=np.ndarray>

      description :

      potential temperature using T, S=35

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      units :

      degC

      Array Chunk Bytes 1.20 MiB 766.14 kiB Shape (157800,) (98066,) Dask graph 2 chunks in 5 graph layers Data type float64 numpy.ndarray

    • Tflx_dia_diff
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      ocean_vertical_diffusive_heat_flux

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • Rif
      (time, depthchi)
      float64
      dask.array<chunksize=(98066, 6), meta=np.ndarray>

      standard_name :

      flux_richardson_number

      Array Chunk Bytes 7.22 MiB 4.49 MiB Shape (157800, 6) (98066, 6) Dask graph 2 chunks in 53 graph layers Data type float64 numpy.ndarray

    • Jb
      (time, depthchi)
      float64
      dask.array<chunksize=(98066, 6), meta=np.ndarray>

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      Array Chunk Bytes 7.22 MiB 4.49 MiB Shape (157800, 6) (98066, 6) Dask graph 2 chunks in 50 graph layers Data type float64 numpy.ndarray

  • Attributes: (10)

    CREATION_DATE :

    23:26 24-FEB-2021

    Data_Source :

    Global Tropical Moored Buoy Array Project Office/NOAA/PMEL

    File_info :

    Contact: Dai.C.McClurg@noaa.gov

    Request_for_acknowledgement :

    If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115

    _FillValue :

    1.0000000409184788e+35

    array :

    TAO/TRITON

    missing_value :

    1.0000000409184788e+35

    platform_code :

    0n165e

    site_code :

    0n165e

    wmo_platform_code :

    52321

  <xarray.DatasetView>
  Dimensions:           (time: 149184, zi: 27, zl: 27, nv: 2)
  Coordinates: (12/16)
    * nv                (nv) float64 1.0 2.0
    * time              (time) datetime64[ns] 2000-01-01T00:30:00 ... 2017-12-3...
      xh                float64 -140.0
      yh                float64 0.0625
      yq                float64 -0.0625
    * zi                (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0
      ...                ...
      oni               (time) float32 -1.318 -1.318 -1.318 ... -0.04526 -0.04526
      en_mask           (time) bool False False False False ... False False False
      ln_mask           (time) bool True True True True ... False False False
      warm_mask         (time) bool False False False False ... True True True
      cool_mask         (time) bool True True True True ... False False False
      enso_transition   (time) <U12 'La-Nina cool' ... '____________'
  Data variables: (12/38)
      KPP_OBLdepth      (time) float32 dask.array<chunksize=(149184,), meta=np.ndarray>
      KPP_buoyFlux      (time, zi) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray>
      KPP_kheat         (time, zi) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray>
      Kd_heat           (time, zi) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray>
      Kv_u              (time, zl) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray>
      Kv_v              (time, zl) float32 dask.array<chunksize=(1080, 27), meta=np.ndarray>
      ...                ...
      Jq                (time, zi) float64 dask.array<chunksize=(1080, 27), meta=np.ndarray>
      shear_prod        (time, zi) float32 dask.array<chunksize=(1080, 26), meta=np.ndarray>
      eps               (time, zi) float32 dask.array<chunksize=(1080, 26), meta=np.ndarray>
      chi               (time, zi) float32 dask.array<chunksize=(1080, 26), meta=np.ndarray>
      Rif               (time, zi) float32 dask.array<chunksize=(1080, 26), meta=np.ndarray>
      sst               (time) float32 dask.array<chunksize=(1080,), meta=np.ndarray>
  Attributes:
      title:    baseline

  baseline

  • Groups: (0)
  • Dimensions:
    • time: 149184
    • zi: 27
    • zl: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      cartesian_axis :

      N

      long_name :

      vertex number

      units :

      none

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2000-01-01T00:30:00 ... 2017-12-...

      array(['2000-01-01T00:30:00.000000000', '2000-01-01T01:30:00.000000000',
             '2000-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      cartesian_axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(1080,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.14 MiB 68.44 kiB Shape (149184,) (8760,) Dask graph 18 chunks in 22 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(1080,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.14 MiB 68.44 kiB Shape (149184,) (8760,) Dask graph 18 chunks in 24 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 1080), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 3.84 MiB 230.98 kiB Shape (27, 149184) (27, 8760) Dask graph 18 chunks in 57 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      -1.318 -1.318 ... -0.04526 -0.04526

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([-1.3176155 , -1.3176155 , -1.3176155 , ..., -0.04525503,
             -0.04525503, -0.04525503], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • warm_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      'La-Nina cool' ... '____________'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
             '____________', '____________', '____________'], dtype='<U12')

  • Data variables: (38)
    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(149184,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_kheat
      (time, zi)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Heat diffusivity due to KPP, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      ocean_vertical_heat_diffusivity

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      standard_name :

      ocean_vertical_y_viscosity

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(149184,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      standard_name :

      ocean_vertical_diffusive_heat_flux

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(149184,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(149184,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(149184,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(149184,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 15.37 MiB 0.90 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $N^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 28 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 28 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 51 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 67 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $Ri^g$

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 66 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(149184,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 582.75 kiB 582.75 kiB Shape (149184,) (149184,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 63 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(1080, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 30.73 MiB 1.80 MiB Shape (149184, 27) (8760, 27) Dask graph 18 chunks in 7 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 72 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 122 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 31 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(1080, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      flux_richardson_number

      Array Chunk Bytes 15.37 MiB 889.69 kiB Shape (149184, 27) (8760, 26) Dask graph 36 chunks in 123 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(1080,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 582.75 kiB 34.22 kiB Shape (149184,) (8760,) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    baseline

  <xarray.DatasetView>
  Dimensions:                         (time: 130968, zi: 27, zl: 27, nv: 2)
  Coordinates: (12/16)
    * nv                              (nv) float64 1.0 2.0
    * time                            (time) datetime64[ns] 2003-01-07T00:30:00...
      xh                              float64 -140.0
      yh                              float64 0.0625
      yq                              float64 -0.0625
    * zi                              (zi) float64 -230.8 -212.0 ... -2.5 -0.0
      ...                              ...
      oni                             (time) float32 nan nan ... 0.09026 0.09026
      en_mask                         (time) bool False False ... False False
      ln_mask                         (time) bool False False ... False False
      warm_mask                       (time) bool True True True ... True True
      cool_mask                       (time) bool False False ... False False
      enso_transition                 (time) <U12 '____________' ... '_________...
  Data variables: (12/53)
      KPP_N2                          (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLT_temp_budget             (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLtransport_heat            (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_OBLdepth                    (time) float32 dask.array<chunksize=(130968,), meta=np.ndarray>
      KPP_buoyFlux                    (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_kheat                       (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      ...                              ...
      Jq                              (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      shear_prod                      (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      eps                             (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      chi                             (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      Rif                             (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      sst                             (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • time: 130968
    • zi: 27
    • zl: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      cartesian_axis :

      N

      long_name :

      vertex number

      units :

      none

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-07T00:30:00 ... 2017-12-...

      array(['2003-01-07T00:30:00.000000000', '2003-01-07T01:30:00.000000000',
             '2003-01-07T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      cartesian_axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 22 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 24 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 3.37 MiB 230.98 kiB Shape (27, 130968) (27, 8760) Dask graph 15 chunks in 57 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      nan nan nan ... 0.09026 0.09026

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([       nan,        nan,        nan, ..., 0.09026337, 0.09026337,
             0.09026337], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... '____________'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             '____________', '____________', '____________'], dtype='<U12')

  • Data variables: (53)
    • KPP_N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_NLT_temp_budget
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat content change due to non-local transport, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_kheat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Heat diffusivity due to KPP, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • N2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • S2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of vertical shear used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • T_advection_xy
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Horizontal convergence of residual mean advective fluxes of heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • T_lbdxy_cont_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Lateral diffusion tracer content tendency for T

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      standard_name :

      ocean_vertical_diffusive_heat_flux

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Th_tendency_vert_remap
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Vertical remapping tracer content tendency for Heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • boundary_forcing_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Boundary forcing heat tendency

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • frazil_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat tendency due to frazil formation

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • opottempdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • opottemppmdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • opottemptend
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of Sea Water Potential Temperature Expressed as Heat Content

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 13.49 MiB 0.90 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 28 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 51 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 67 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 56 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 63 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 26.98 MiB 1.80 MiB Shape (130968, 27) (8760, 27) Dask graph 15 chunks in 7 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 72 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 31 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 13.49 MiB 889.69 kiB Shape (130968, 27) (8760, 26) Dask graph 30 chunks in 123 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 511.59 kiB 34.22 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:               (time: 157680, zl: 27, zi: 27, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2000-01-01T00:30:00 ... 2017-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 -1.277 -1.277 -1.277 ... nan nan nan
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool True True True True ... False False False
      warm_mask             (time) bool False False False False ... True True True
      cool_mask             (time) bool True True True True ... False False False
      enso_transition       (time) <U12 'La-Nina cool' ... 'El-Nino warm'
  Data variables: (12/48)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(157680,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(157680,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      ...                    ...
      Jq                    (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KD=0, KV=0

  new_baseline.hb

  • Groups: (0)
  • Dimensions:
    • time: 157680
    • zl: 27
    • zi: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2000-01-01T00:30:00 ... 2017-12-...

      array(['2000-01-01T00:30:00.000000000', '2000-01-01T01:30:00.000000000',
             '2000-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.20 MiB 68.44 kiB Shape (157680,) (8760,) Dask graph 18 chunks in 22 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.20 MiB 68.44 kiB Shape (157680,) (8760,) Dask graph 18 chunks in 24 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 4.06 MiB 230.98 kiB Shape (27, 157680) (27, 8760) Dask graph 18 chunks in 57 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      -1.277 -1.277 -1.277 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([-1.2772478, -1.2772478, -1.2772478, ...,        nan,        nan,
                    nan], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • warm_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      'La-Nina cool' ... 'El-Nino warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['La-Nina cool', 'La-Nina cool', 'La-Nina cool', ...,
             'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')

  • Data variables: (48)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 16.24 MiB 0.90 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 24 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 28 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 51 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 67 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 56 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(157680,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 615.94 kiB 615.94 kiB Shape (157680,) (157680,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 63 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 32.48 MiB 1.80 MiB Shape (157680, 27) (8760, 27) Dask graph 18 chunks in 7 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 72 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 122 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 31 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 16.24 MiB 889.69 kiB Shape (157680, 27) (8760, 26) Dask graph 36 chunks in 123 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 615.94 kiB 34.22 kiB Shape (157680,) (8760,) Dask graph 18 chunks in 6 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KD=0, KV=0

  <xarray.DatasetView>
  Dimensions:               (time: 113880, zl: 27, zi: 27, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2003-01-01T00:30:00 ... 2015-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 nan nan nan nan nan ... nan nan nan nan
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool False False False ... False False False
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 '____________' ... 'El-Nino warm'
  Data variables: (12/48)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(113880,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      ...                    ...
      Jq                    (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  new_baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • time: 113880
    • zl: 27
    • zi: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2015-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2015-12-31T21:30:00.000000000',
             '2015-12-31T22:30:00.000000000', '2015-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 889.69 kiB 68.44 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 2.93 MiB 230.98 kiB Shape (27, 113880) (27, 8760) Dask graph 13 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      nan nan nan nan ... nan nan nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... 'El-Nino warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')

  • Data variables: (48)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 11.73 MiB 0.90 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(113880,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 444.84 kiB 444.84 kiB Shape (113880,) (113880,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 23.46 MiB 1.80 MiB Shape (113880, 27) (8760, 27) Dask graph 13 chunks in 6 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 11.73 MiB 889.69 kiB Shape (113880, 27) (8760, 26) Dask graph 26 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 444.84 kiB 34.22 kiB Shape (113880,) (8760,) Dask graph 13 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:               (time: 105120, zl: 27, zi: 27, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2003-01-01T00:30:00 ... 2014-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -230.8 -212.0 -194.4 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 nan nan nan nan nan ... nan nan nan nan
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool False False False ... False False False
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 '____________' ... '____________'
  Data variables: (12/48)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(105120,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      ...                    ...
      Jq                    (time, zi) float64 dask.array<chunksize=(8760, 27), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 26), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ri0=0.5

  new_baseline.kpp.lmd.005

  • Groups: (0)
  • Dimensions:
    • time: 105120
    • zl: 27
    • zi: 27
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2014-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2014-12-31T21:30:00.000000000',
             '2014-12-31T22:30:00.000000000', '2014-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -230.8 -212.0 -194.4 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66,
             -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,  -49.94,  -42.66,
              -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,  -10.  ,   -7.5 ,
               -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -240.8 -221.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-240.79 , -221.4  , -203.215, -186.13 , -170.055, -154.915, -140.645,
             -127.19 , -114.515, -102.6  ,  -91.425,  -80.98 ,  -71.255,  -62.24 ,
              -53.925,  -46.3  ,  -39.355,  -33.075,  -27.455,  -22.485,  -18.155,
              -14.46 ,  -11.385,   -8.75 ,   -6.25 ,   -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 821.25 kiB 68.44 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 821.25 kiB 68.44 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(27, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 2.71 MiB 230.98 kiB Shape (27, 105120) (27, 8760) Dask graph 12 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      nan nan nan nan ... nan nan nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      ONI

      standard_name :

      oceanic_nino_index

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... '____________'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             '____________', '____________', '____________'], dtype='<U12')

  • Data variables: (48)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 10.83 MiB 0.90 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(105120,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 410.62 kiB 410.62 kiB Shape (105120,) (105120,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 27), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 21.65 MiB 1.80 MiB Shape (105120, 27) (8760, 27) Dask graph 12 chunks in 6 graph layers Data type float64 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 26), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 10.83 MiB 889.69 kiB Shape (105120, 27) (8760, 26) Dask graph 24 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 410.62 kiB 34.22 kiB Shape (105120,) (8760,) Dask graph 12 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ri0=0.5

• Dimensions:
• Coordinates: (0)
• Data variables: (0)
• Attributes: (0)

ref = tree["TAO"].ds.reset_coords(drop=True).cf.sel(Z=slice(-120, None))
counts = np.minimum(ref["S2"].cf.count("Z"), ref["N2T"].cf.count("Z")).load()


def calc_histograms(ds):
    ds = ds.copy()
    ds["tao_mask"] = counts.reindex(time=ds.time, method="nearest") > 5
    ds["tao_mask"].attrs = {
        "description": "True when there are more than 5 5-m T, u, v in TAO dataset"
    }
    # ds = ds.where(ds.tao_mask)
    return ds.update(mixpods.pdf_N2S2(ds))


tree = tree.map_over_subtree(calc_histograms)

daily composite

%autoreload

dailies = tree.map_over_subtree(mixpods.daily_composites)
dailies

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,

<xarray.DatasetView>
Dimensions:  ()
Data variables:
    *empty*

datatree.DataTree

• Groups: (6)
  <xarray.DatasetView>
  Dimensions:             (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth               (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      latitude            float32 0.0
      longitude           float32 -140.0
      reference_pressure  int64 0
    * hour                (hour) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23
    * tau_bins            (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT                  (depth, hour, tau_bins) float64 9.959e-06 ... 0.0001646
      eps                 (depth, hour, tau_bins) float64 4.205e-09 ... 1.608e-08
      chi                 (depth, hour, tau_bins) float64 1.67e-08 ... 1.95e-08
      Jb                  (depth, hour, tau_bins) float64 nan nan nan ... nan nan
      Jq                  (depth, hour, tau_bins) float64 -1.003 -1.842 ... -4.428
      S2                  (depth, hour, tau_bins) float32 0.0001345 ... 0.0001682
      N2                  (depth, hour, tau_bins) float64 0.0002082 ... 2.895e-05
      Rig                 (depth, hour, tau_bins) float64 1.406 1.252 ... 0.05421
      Rig_T               (depth, hour, tau_bins) float64 1.901 1.204 ... 0.1185
      tau                 (hour, tau_bins) float64 0.02709 0.05733 ... 0.09308
  Attributes:
      CREATION_DATE:                23:26 24-FEB-2021
      Data_Source:                  Global Tropical Moored Buoy Array Project O...
      File_info:                    Contact: Dai.C.McClurg@noaa.gov
      Request_for_acknowledgement:  If you use these data in publications or pr...
      _FillValue:                   1.0000000409184788e+35
      array:                        TAO/TRITON
      missing_value:                1.0000000409184788e+35
      platform_code:                0n165e
      site_code:                    0n165e
      wmo_platform_code:            52321

  TAO

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      positive :

      up

      units :

      m

      array([-89., -69., -59., -49., -39., -29.])

    • latitude
      ()
      float32
      0.0

      array(0., dtype=float32)

    • longitude
      ()
      float32
      -140.0

      array(-140., dtype=float32)

    • reference_pressure
      ()
      int64
      0

      array(0)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float64
      9.959e-06 1.107e-05 ... 0.0001646

      standard_name :

      ocean_vertical_heat_diffusivity

      array([[[9.95869314e-06, 1.10713814e-05, 1.81385305e-05],
              [7.62679606e-06, 9.84649659e-06, 1.98903482e-05],
              [8.26769194e-06, 1.03487366e-05, 1.69229065e-05],
              [5.09341517e-06, 1.06415264e-05, 9.94711354e-06],
              [6.27289392e-06, 1.01286448e-05, 9.09556433e-06],
              [5.85044689e-06, 9.09517149e-06, 1.06714061e-05],
              [5.92212630e-06, 8.40077865e-06, 1.11052356e-05],
              [6.47377658e-06, 9.78053302e-06, 1.13559910e-05],
              [5.77007820e-06, 1.00437481e-05, 1.54385405e-05],
              [5.60030739e-06, 8.32575368e-06, 1.31285365e-05],
              [5.27227053e-06, 7.15577324e-06, 1.44267402e-05],
              [5.03200713e-06, 9.93609875e-06, 1.68541630e-05],
              [6.42531180e-06, 1.04701955e-05, 1.45938509e-05],
              [5.45290240e-06, 1.06380336e-05, 1.94790247e-05],
              [5.05695470e-06, 1.08870254e-05, 2.13555496e-05],
              [6.78671892e-06, 1.23528905e-05, 2.01087241e-05],
              [9.80084606e-06, 1.38124457e-05, 2.85315836e-05],
              [6.90458131e-06, 1.42042099e-05, 2.59700678e-05],
              [8.44895131e-06, 1.38211283e-05, 2.92244979e-05],
              [6.96328801e-06, 1.80749482e-05, 2.81233184e-05],
      ...
              [2.49796366e-05, 1.51222154e-04, 1.54133302e-03],
              [2.54375198e-05, 1.92556637e-04, 1.98323078e-03],
              [2.95016029e-05, 2.74405783e-04, 2.23706350e-03],
              [2.91381387e-05, 5.29042782e-04, 2.46231496e-03],
              [3.22679193e-05, 6.25229486e-04, 2.36063586e-03],
              [4.87863828e-05, 6.82247064e-04, 2.44611497e-03],
              [7.65505437e-05, 8.72437582e-04, 2.08043411e-03],
              [9.60910794e-05, 7.32102430e-04, 2.33006858e-03],
              [8.45055247e-05, 8.06508643e-04, 1.81524742e-03],
              [9.68881747e-05, 7.19295971e-04, 2.07390795e-03],
              [1.12205990e-04, 8.31837440e-04, 1.82439093e-03],
              [1.25092879e-04, 8.00568096e-04, 1.85697065e-03],
              [1.41021393e-04, 7.84944210e-04, 1.71427588e-03],
              [1.35184262e-04, 7.04609354e-04, 1.41404584e-03],
              [1.12211968e-04, 3.97215676e-04, 1.07973119e-03],
              [8.04180511e-05, 2.89701497e-04, 6.65654642e-04],
              [6.64714222e-05, 1.94172831e-04, 4.08622947e-04],
              [5.75134301e-05, 1.40309406e-04, 2.67942773e-04],
              [5.13853203e-05, 1.01548040e-04, 1.64550217e-04],
              [4.10933487e-05, 8.66966898e-05, 1.64556856e-04]]])

    • eps
      (depth, hour, tau_bins)
      float64
      4.205e-09 6.427e-09 ... 1.608e-08

      array([[[4.20484047e-09, 6.42665336e-09, 1.03495228e-08],
              [3.39942155e-09, 6.09951608e-09, 9.95406422e-09],
              [3.22205779e-09, 6.00608012e-09, 9.89785322e-09],
              [2.43384740e-09, 5.65188162e-09, 6.43540665e-09],
              [2.34056719e-09, 4.68248253e-09, 5.86884639e-09],
              [2.42516755e-09, 4.36594516e-09, 6.04769734e-09],
              [2.01203910e-09, 4.50894201e-09, 6.80387559e-09],
              [2.77474401e-09, 4.06819005e-09, 5.78686391e-09],
              [3.02695691e-09, 4.19429197e-09, 9.25259960e-09],
              [2.16803642e-09, 4.07562895e-09, 7.99217582e-09],
              [2.25024698e-09, 3.86947366e-09, 1.08315576e-08],
              [2.34519609e-09, 4.58983072e-09, 8.80408647e-09],
              [2.43251603e-09, 5.04441870e-09, 1.00646850e-08],
              [2.87316856e-09, 4.50655083e-09, 1.06291960e-08],
              [3.21955035e-09, 5.40452670e-09, 1.13614463e-08],
              [2.93556264e-09, 5.97819732e-09, 1.35444442e-08],
              [3.32274328e-09, 6.36330330e-09, 1.69035479e-08],
              [3.41472292e-09, 7.45018817e-09, 1.40619889e-08],
              [4.16563716e-09, 6.50261284e-09, 1.49772749e-08],
              [3.93711329e-09, 8.03877507e-09, 1.71921395e-08],
      ...
              [4.32062308e-09, 2.14336097e-08, 1.84017958e-07],
              [4.32244969e-09, 3.23795526e-08, 1.77984766e-07],
              [4.99443371e-09, 4.76849714e-08, 1.82287235e-07],
              [6.67475746e-09, 7.43192793e-08, 1.80947783e-07],
              [7.69190689e-09, 8.48603912e-08, 1.83042578e-07],
              [1.24068720e-08, 8.70767941e-08, 1.63145058e-07],
              [1.75400970e-08, 9.81732289e-08, 1.55381279e-07],
              [2.07108437e-08, 8.34432489e-08, 1.47163501e-07],
              [2.09570176e-08, 9.55806383e-08, 1.28104332e-07],
              [2.49357959e-08, 7.48081255e-08, 1.38691872e-07],
              [2.02488017e-08, 7.54215709e-08, 1.17639124e-07],
              [2.72920187e-08, 7.42685031e-08, 1.22579504e-07],
              [2.84401490e-08, 7.54687544e-08, 1.20862216e-07],
              [2.32893736e-08, 6.57975258e-08, 9.00575173e-08],
              [1.80033299e-08, 4.22211119e-08, 6.23893426e-08],
              [1.57912073e-08, 2.80175873e-08, 4.02353834e-08],
              [1.23950854e-08, 2.20880606e-08, 2.95446015e-08],
              [1.11499973e-08, 1.59336048e-08, 1.98472383e-08],
              [8.36356549e-09, 1.27149248e-08, 1.63302630e-08],
              [7.16264221e-09, 1.10760452e-08, 1.60790152e-08]]])

    • chi
      (depth, hour, tau_bins)
      float64
      1.67e-08 3.739e-08 ... 1.95e-08

      array([[[1.67021892e-08, 3.73895360e-08, 7.93282652e-08],
              [1.45695729e-08, 3.21540306e-08, 7.51363908e-08],
              [1.66509708e-08, 3.17736964e-08, 7.22932589e-08],
              [1.00107661e-08, 3.13534986e-08, 4.77883670e-08],
              [1.33179056e-08, 2.40084728e-08, 3.57552816e-08],
              [9.22712244e-09, 2.50863744e-08, 4.33211842e-08],
              [8.08744573e-09, 2.38605289e-08, 3.82013297e-08],
              [1.09612925e-08, 2.27882209e-08, 3.99283958e-08],
              [1.34920747e-08, 1.97982703e-08, 4.72313840e-08],
              [9.85650711e-09, 2.04048889e-08, 5.14985775e-08],
              [1.08937936e-08, 2.35725432e-08, 6.87134261e-08],
              [1.21212706e-08, 2.51579652e-08, 7.15330504e-08],
              [1.32546719e-08, 3.39587720e-08, 9.47722781e-08],
              [1.69953873e-08, 2.60973815e-08, 7.29116184e-08],
              [1.67531863e-08, 3.07021553e-08, 6.79356643e-08],
              [1.77120188e-08, 4.09737308e-08, 7.29900722e-08],
              [1.93716942e-08, 4.31379369e-08, 9.26064400e-08],
              [2.09236218e-08, 4.34670007e-08, 9.19779817e-08],
              [2.48015149e-08, 3.95476993e-08, 8.28554984e-08],
              [2.05587984e-08, 5.27063058e-08, 8.39105570e-08],
      ...
              [6.58562963e-09, 4.00100193e-08, 1.82930124e-07],
              [8.27051334e-09, 5.94085776e-08, 1.62815748e-07],
              [1.05461194e-08, 7.08934926e-08, 1.68249950e-07],
              [1.42336592e-08, 9.11751999e-08, 1.81170217e-07],
              [1.72444375e-08, 9.91038146e-08, 1.50109762e-07],
              [2.48941347e-08, 1.02137827e-07, 1.58365512e-07],
              [3.49353545e-08, 1.15828778e-07, 1.27671006e-07],
              [4.11614687e-08, 9.85426121e-08, 1.36028494e-07],
              [3.54397051e-08, 9.90287508e-08, 1.15470101e-07],
              [4.15427310e-08, 8.94060867e-08, 1.12016269e-07],
              [3.92166332e-08, 8.64900353e-08, 9.95322983e-08],
              [5.45326891e-08, 8.52935771e-08, 1.10918666e-07],
              [4.42275054e-08, 7.57406371e-08, 9.45690680e-08],
              [4.10326472e-08, 7.06337841e-08, 7.52282802e-08],
              [3.53795830e-08, 4.58133432e-08, 5.44219219e-08],
              [2.37814091e-08, 3.15729107e-08, 3.78253266e-08],
              [1.95929775e-08, 2.64875960e-08, 3.30521637e-08],
              [1.66729435e-08, 2.15147840e-08, 2.22266528e-08],
              [1.50428825e-08, 1.53505017e-08, 1.73559861e-08],
              [1.08272541e-08, 1.40830926e-08, 1.94961160e-08]]])

    • Jb
      (depth, hour, tau_bins)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      array([[[nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
      ...
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan]]])

    • Jq
      (depth, hour, tau_bins)
      float64
      -1.003 -1.842 ... -2.971 -4.428

      array([[[ -1.00256446,  -1.84244745,  -2.80850936],
              [ -0.91887084,  -1.62464068,  -2.56282757],
              [ -0.88374135,  -1.58679603,  -2.88823456],
              [ -0.70443327,  -1.6103817 ,  -1.77298658],
              [ -0.68665724,  -1.27295597,  -1.77228   ],
              [ -0.66664796,  -1.19898684,  -1.67626741],
              [ -0.59730391,  -1.23465212,  -1.70404798],
              [ -0.7235757 ,  -1.18544042,  -1.59040206],
              [ -0.75019594,  -1.04231212,  -2.57469427],
              [ -0.57483421,  -1.14463015,  -2.24184293],
              [ -0.63752255,  -1.10632234,  -2.90389878],
              [ -0.65130446,  -1.26093457,  -2.35183439],
              [ -0.66722366,  -1.3861301 ,  -2.72893158],
              [ -0.81851688,  -1.24810229,  -2.85460253],
              [ -0.72343038,  -1.27913762,  -2.93287239],
              [ -0.82057933,  -1.73613097,  -3.35338815],
              [ -0.92375014,  -1.80837082,  -4.44104197],
              [ -0.87323403,  -2.01851581,  -3.77664889],
              [ -1.19708228,  -1.7267686 ,  -4.23530912],
              [ -0.98020427,  -2.19262619,  -4.13656709],
      ...
              [ -1.11297409,  -5.84520527, -47.88722398],
              [ -1.20329183,  -9.40240756, -45.37124552],
              [ -1.33200184, -12.95762863, -46.64407742],
              [ -1.73752661, -19.31055455, -45.85240017],
              [ -2.00999545, -20.92691833, -45.36418532],
              [ -3.38542206, -21.6217159 , -41.41348273],
              [ -4.660082  , -25.72382479, -37.29098972],
              [ -5.53276281, -20.1004968 , -36.00067158],
              [ -5.56787429, -22.40393839, -31.23276616],
              [ -5.82381354, -19.36849336, -34.99038496],
              [ -5.01723603, -19.13635109, -28.69033267],
              [ -6.73045836, -19.73596063, -28.18919034],
              [ -7.09852648, -18.76319064, -30.2266072 ],
              [ -6.12788   , -16.48818028, -21.37988398],
              [ -4.62785644, -10.76205284, -16.19281641],
              [ -4.21420556,  -7.65540787, -10.55800383],
              [ -3.29425731,  -5.96463463,  -7.87434395],
              [ -2.86694385,  -4.28650835,  -5.42953264],
              [ -2.33320233,  -3.38921404,  -4.47756404],
              [ -1.82422195,  -2.97098078,  -4.42772743]]])

    • S2
      (depth, hour, tau_bins)
      float32
      0.0001345 0.0002001 ... 0.0001682

      long_name :

      $S^2$

      array([[[0.00013448, 0.00020006, 0.00030025],
              [0.00013459, 0.00019925, 0.00030133],
              [0.00013651, 0.00019142, 0.0003029 ],
              [0.00012919, 0.00020452, 0.00029948],
              [0.00013896, 0.0002124 , 0.00030397],
              [0.00015213, 0.00019731, 0.00031407],
              [0.00014794, 0.00019219, 0.00030202],
              [0.00015217, 0.00019176, 0.00030711],
              [0.0001403 , 0.00019476, 0.00029934],
              [0.00013978, 0.00018397, 0.00030806],
              [0.00014281, 0.00018728, 0.00030986],
              [0.00014091, 0.00018794, 0.00031204],
              [0.00013823, 0.0001874 , 0.000305  ],
              [0.00013669, 0.00019039, 0.00031266],
              [0.00013547, 0.00019014, 0.00030768],
              [0.0001324 , 0.00018962, 0.00029873],
              [0.00013033, 0.00018335, 0.00029489],
              [0.00013093, 0.0001916 , 0.00030148],
              [0.00014072, 0.00018797, 0.00031962],
              [0.00014378, 0.00018233, 0.00031395],
      ...
              [0.00030681, 0.00022737, 0.00020181],
              [0.00031573, 0.00023622, 0.000205  ],
              [0.00032313, 0.0002395 , 0.00019791],
              [0.00032533, 0.00024731, 0.00017745],
              [0.0003447 , 0.00024576, 0.00016273],
              [0.00036876, 0.0002621 , 0.00016414],
              [0.0003664 , 0.000238  , 0.00016017],
              [0.00036569, 0.00023603, 0.00015653],
              [0.00035129, 0.00023055, 0.00014936],
              [0.00035962, 0.00021455, 0.00015314],
              [0.00033461, 0.00022228, 0.00015203],
              [0.00038251, 0.00024297, 0.00017307],
              [0.00040409, 0.000252  , 0.0001731 ],
              [0.00037549, 0.00023915, 0.00015291],
              [0.00036383, 0.00021962, 0.0001535 ],
              [0.00036171, 0.00020565, 0.0001415 ],
              [0.0003339 , 0.00020438, 0.00015409],
              [0.00035724, 0.00019812, 0.00015214],
              [0.0003461 , 0.00020423, 0.00015108],
              [0.0003532 , 0.00020651, 0.00016817]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float64
      0.0002082 0.0002229 ... 2.895e-05

      long_name :

      $N^2$

      array([[[2.08151738e-04, 2.22862104e-04, 2.14441416e-04],
              [2.16603991e-04, 2.24483495e-04, 2.21728203e-04],
              [2.15355923e-04, 2.21838325e-04, 2.18587475e-04],
              [2.15544742e-04, 2.22885541e-04, 2.12326524e-04],
              [2.16644011e-04, 2.13538050e-04, 2.23611350e-04],
              [2.09853593e-04, 2.20879385e-04, 2.19642997e-04],
              [1.98066216e-04, 2.21105441e-04, 2.18486263e-04],
              [2.16155790e-04, 2.18854377e-04, 2.23897831e-04],
              [2.21096656e-04, 2.18472571e-04, 2.21506161e-04],
              [2.16910676e-04, 2.20828730e-04, 2.15561007e-04],
              [2.20007830e-04, 2.25418650e-04, 2.14505446e-04],
              [2.13170636e-04, 2.18209400e-04, 2.26985607e-04],
              [2.08379942e-04, 2.20359529e-04, 2.28006372e-04],
              [2.18869956e-04, 2.12548021e-04, 2.37007497e-04],
              [2.22657337e-04, 2.18700017e-04, 2.19859678e-04],
              [2.16105074e-04, 2.18497812e-04, 2.29437082e-04],
              [2.15577658e-04, 2.11075774e-04, 2.20386303e-04],
              [2.14909841e-04, 2.19245199e-04, 2.10944560e-04],
              [2.18744613e-04, 2.23280408e-04, 2.02637232e-04],
              [2.18981413e-04, 2.22255352e-04, 2.07395227e-04],
      ...
              [6.96024719e-05, 4.77216368e-05, 3.52536021e-05],
              [7.21013450e-05, 4.76598289e-05, 3.29697584e-05],
              [7.22574894e-05, 5.01282745e-05, 3.12980790e-05],
              [7.68367868e-05, 4.81516730e-05, 3.00344601e-05],
              [7.68827915e-05, 4.78022934e-05, 2.65751683e-05],
              [7.66054714e-05, 4.80892891e-05, 2.51018158e-05],
              [7.63678736e-05, 4.39940175e-05, 2.65891577e-05],
              [7.64221137e-05, 4.27375694e-05, 2.65801164e-05],
              [7.63855405e-05, 4.13222946e-05, 2.54512380e-05],
              [7.23437008e-05, 3.95318950e-05, 2.40070337e-05],
              [7.32406023e-05, 3.87474723e-05, 2.17285776e-05],
              [7.84975393e-05, 3.86002397e-05, 2.22512416e-05],
              [7.96900786e-05, 3.91479868e-05, 2.12247197e-05],
              [7.60129546e-05, 3.42026320e-05, 2.17803309e-05],
              [7.44090646e-05, 3.69594533e-05, 2.22136917e-05],
              [7.49782129e-05, 3.66955626e-05, 2.24673463e-05],
              [7.37285223e-05, 3.66268637e-05, 2.49871805e-05],
              [7.33997174e-05, 3.74016674e-05, 2.55136179e-05],
              [7.08907159e-05, 4.04018172e-05, 2.61858948e-05],
              [6.88821220e-05, 4.19587334e-05, 2.89518715e-05]]])

    • Rig
      (depth, hour, tau_bins)
      float64
      1.406 1.252 ... 0.07694 0.05421

      long_name :

      $Ri^g$

      array([[[1.40644738, 1.25178115, 0.74488556],
              [1.41762533, 1.35060308, 0.76782803],
              [1.47274045, 1.28796047, 0.71477707],
              [1.77206894, 1.23581944, 0.85990554],
              [1.42856916, 1.18887077, 0.86747588],
              [1.37627981, 1.250239  , 0.79682319],
              [1.35225077, 1.14314482, 0.89240256],
              [1.53488295, 1.13362321, 0.81762861],
              [1.43149522, 1.18854333, 0.82108334],
              [1.69124541, 1.31780546, 0.74364754],
              [1.63997589, 1.2650186 , 0.75719731],
              [1.60196071, 1.29321876, 0.80676174],
              [1.76211032, 1.29848346, 0.82638659],
              [1.89364181, 1.2401134 , 0.81697784],
              [1.75861073, 1.2281106 , 0.82555718],
              [1.79334254, 1.25421119, 0.87983679],
              [1.75460255, 1.23742487, 0.81284393],
              [1.6443649 , 1.20305246, 0.80500277],
              [1.90235508, 1.39715558, 0.75862198],
              [1.59556172, 1.27929263, 0.69249317],
      ...
              [0.11922037, 0.08834196, 0.05390135],
              [0.11364714, 0.0985151 , 0.04969669],
              [0.11955433, 0.09013164, 0.04259646],
              [0.13152116, 0.08148522, 0.04038412],
              [0.12462708, 0.07752266, 0.0412092 ],
              [0.1059685 , 0.08015321, 0.03483511],
              [0.11336615, 0.07559725, 0.03430897],
              [0.10954652, 0.07044978, 0.03097055],
              [0.10013768, 0.07146289, 0.03221148],
              [0.08570449, 0.07030557, 0.0349513 ],
              [0.09017043, 0.06546468, 0.03168679],
              [0.08669634, 0.0582093 , 0.03019886],
              [0.09715547, 0.06009826, 0.02640776],
              [0.07935822, 0.05776852, 0.03262731],
              [0.09296572, 0.05099493, 0.03820643],
              [0.08410832, 0.06245423, 0.04184208],
              [0.10067066, 0.0679023 , 0.04833413],
              [0.10772209, 0.06960988, 0.05154636],
              [0.096249  , 0.07780057, 0.05655469],
              [0.10424006, 0.07694161, 0.05421351]]])

    • Rig_T
      (depth, hour, tau_bins)
      float64
      1.901 1.204 0.6325 ... 0.12 0.1185

      long_name :

      $Ri^g_T$

      array([[[1.90144926, 1.20402004, 0.63250394],
              [1.97722436, 1.16803555, 0.63929729],
              [1.82363029, 1.19271362, 0.63758984],
              [2.00858352, 1.17413288, 0.64556353],
              [1.764249  , 1.08857607, 0.63981687],
              [1.6722678 , 1.14029434, 0.63841363],
              [1.64416596, 1.07404686, 0.69016643],
              [1.80985361, 1.1286188 , 0.66627749],
              [1.84750455, 1.11619713, 0.65720696],
              [1.85832509, 1.14625708, 0.64646297],
              [1.82383741, 1.28825209, 0.60547409],
              [1.90534517, 1.22815954, 0.65631865],
              [1.92757436, 1.17589125, 0.6729475 ],
              [1.95646798, 1.15944217, 0.67811545],
              [2.15228629, 1.2346199 , 0.68925813],
              [2.17114092, 1.25440367, 0.67767255],
              [1.9283005 , 1.25106341, 0.65996391],
              [2.09583235, 1.16882993, 0.64695664],
              [2.00001933, 1.18052973, 0.63612982],
              [1.95096458, 1.24909609, 0.60900943],
      ...
              [0.14035465, 0.15645461, 0.13741426],
              [0.15093877, 0.1540195 , 0.12269896],
              [0.1517013 , 0.14701272, 0.10974087],
              [0.15332836, 0.13742055, 0.10176559],
              [0.14785674, 0.1249745 , 0.09094855],
              [0.14468148, 0.11747535, 0.08905629],
              [0.1371656 , 0.11502819, 0.08797114],
              [0.13831776, 0.10755145, 0.08060554],
              [0.1255792 , 0.1066724 , 0.07354782],
              [0.11734482, 0.09774099, 0.07285993],
              [0.12316585, 0.0914999 , 0.07129744],
              [0.11004655, 0.0806289 , 0.05700402],
              [0.12113939, 0.07903602, 0.04899825],
              [0.11156332, 0.08470378, 0.06233322],
              [0.11291802, 0.09061568, 0.07497081],
              [0.12169541, 0.09945144, 0.08995757],
              [0.12816946, 0.11179433, 0.0962672 ],
              [0.13384635, 0.11637368, 0.11296923],
              [0.12542545, 0.12154479, 0.11954271],
              [0.12356876, 0.12001706, 0.11848762]]])

    • tau
      (hour, tau_bins)
      float64
      0.02709 0.05733 ... 0.05745 0.09308

      array([[0.0270877 , 0.05732647, 0.09203958],
             [0.02680315, 0.05759896, 0.09214987],
             [0.0266931 , 0.05764483, 0.09244683],
             [0.02699333, 0.05713952, 0.09263131],
             [0.02688743, 0.0573335 , 0.09261309],
             [0.02682686, 0.05738899, 0.0923755 ],
             [0.02736078, 0.05730989, 0.09163058],
             [0.02709459, 0.05719675, 0.09233963],
             [0.0260143 , 0.05757715, 0.09312405],
             [0.02643325, 0.05748537, 0.09276128],
             [0.02604512, 0.05695688, 0.09281864],
             [0.02653221, 0.05750187, 0.09254788],
             [0.02541264, 0.05698433, 0.09270642],
             [0.0258142 , 0.0573662 , 0.09281572],
             [0.02577848, 0.05747551, 0.09283653],
             [0.02582663, 0.05766199, 0.09305366],
             [0.0262083 , 0.05818581, 0.09442765],
             [0.02586172, 0.05809102, 0.0939246 ],
             [0.02600226, 0.05773976, 0.09318213],
             [0.02695185, 0.05763241, 0.09303027],
             [0.02669914, 0.05756156, 0.09276316],
             [0.02679048, 0.05723235, 0.09331579],
             [0.02705673, 0.05729915, 0.09280436],
             [0.02745168, 0.05745369, 0.09308222]])

  • Attributes: (10)

    CREATION_DATE :

    23:26 24-FEB-2021

    Data_Source :

    Global Tropical Moored Buoy Array Project Office/NOAA/PMEL

    File_info :

    Contact: Dai.C.McClurg@noaa.gov

    Request_for_acknowledgement :

    If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115

    _FillValue :

    1.0000000409184788e+35

    array :

    TAO/TRITON

    missing_value :

    1.0000000409184788e+35

    platform_code :

    0n165e

    site_code :

    0n165e

    wmo_platform_code :

    52321

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006551
      eps       (depth, hour, tau_bins) float32 2.922e-08 1.368e-07 ... 2.348e-08
      chi       (depth, hour, tau_bins) float32 3.153e-08 5.147e-08 ... 5.686e-09
      Jb        (depth, hour, tau_bins) float32 -3.583e-10 ... -4.18e-10
      Jq        (depth, hour, tau_bins) float64 -0.5587 -0.7393 ... -28.37 -5.81
      S2        (depth, hour, tau_bins) float32 0.000118 0.0002866 ... 2.19e-05
      N2        (depth, hour, tau_bins) float32 0.0002707 0.0002679 ... 1.066e-05
      Rig       (depth, hour, tau_bins) float32 1.866 0.6854 ... 0.4224 0.5125
      Rig_T     (depth, hour, tau_bins) float32 1.75 0.6778 0.516 ... 0.2708 0.353
      tau       (hour, tau_bins) float32 0.0295 0.054 0.08233 ... 0.05439 0.08311
  Attributes:
      title:    baseline

  baseline

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 1.001e-06 ... 0.0006551

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      ocean_vertical_heat_diffusivity

      array([[[1.00062630e-06, 1.00062675e-06, 1.00062948e-06],
              [1.00062630e-06, 1.00062675e-06, 1.00062914e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00070815e-06],
              [1.00062630e-06, 1.00062675e-06, 1.00062823e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062800e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062800e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062812e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062857e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062869e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062857e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00570742e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00063801e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00505042e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00063016e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062857e-06],
              [1.00062630e-06, 1.00062653e-06, 1.00062778e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062721e-06],
              [1.00062630e-06, 1.00062653e-06, 1.00062732e-06],
              [1.00062630e-06, 1.00062653e-06, 1.00062744e-06],
              [1.00062618e-06, 1.00062664e-06, 1.00062789e-06],
      ...
              [1.31017505e-03, 1.84910290e-03, 3.04656965e-03],
              [1.20016653e-03, 1.93014904e-03, 2.43997667e-02],
              [1.26303639e-03, 2.55707884e-03, 3.35960016e-02],
              [1.21755805e-03, 3.45059624e-03, 4.70925234e-02],
              [1.20411417e-03, 6.63228473e-03, 5.48477545e-02],
              [1.20652630e-03, 1.19018322e-02, 6.12227395e-02],
              [1.33580097e-03, 1.68863572e-02, 6.64590597e-02],
              [1.38179970e-03, 2.08421741e-02, 7.17242733e-02],
              [1.61000830e-03, 2.49606222e-02, 7.40951598e-02],
              [1.71344716e-03, 2.64077000e-02, 7.43862391e-02],
              [1.82845863e-03, 2.77032442e-02, 7.27289319e-02],
              [1.99175603e-03, 2.91718896e-02, 7.13082328e-02],
              [1.64016616e-03, 1.40310498e-02, 5.11850938e-02],
              [2.00258894e-03, 2.12116283e-03, 1.37765761e-02],
              [1.67123857e-03, 1.55570242e-03, 4.48137894e-03],
              [1.43854087e-03, 6.79666526e-04, 3.39539582e-03],
              [2.04196898e-03, 1.61034847e-03, 7.48192775e-04],
              [2.00651051e-03, 1.96600612e-03, 5.42554015e-04],
              [1.92917790e-03, 1.86887372e-03, 4.04946972e-04],
              [1.80337648e-03, 1.76201062e-03, 6.55124430e-04]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      2.922e-08 1.368e-07 ... 2.348e-08

      long_name :

      $SP$

      units :

      W/kg

      array([[[2.92183362e-08, 1.36777288e-07, 2.27541989e-07],
              [2.90522504e-08, 1.39135309e-07, 2.38471500e-07],
              [3.01237151e-08, 1.37680928e-07, 2.37547908e-07],
              [3.13189972e-08, 1.36566356e-07, 2.33948427e-07],
              [3.05815284e-08, 1.31672735e-07, 2.26799813e-07],
              [2.99091028e-08, 1.30052570e-07, 2.21252151e-07],
              [2.86628925e-08, 1.24942943e-07, 2.08034521e-07],
              [2.91027824e-08, 1.22521584e-07, 2.01086578e-07],
              [3.04016972e-08, 1.19735688e-07, 1.95425770e-07],
              [3.16648112e-08, 1.19069099e-07, 1.88375097e-07],
              [3.19412052e-08, 1.16080244e-07, 1.91520044e-07],
              [3.19991642e-08, 1.15435903e-07, 1.95864132e-07],
              [3.21174944e-08, 1.14669440e-07, 1.85658010e-07],
              [3.06349754e-08, 1.09434126e-07, 1.88835344e-07],
              [2.94250633e-08, 1.05647260e-07, 1.79058816e-07],
              [2.91206543e-08, 1.01917209e-07, 1.72599258e-07],
              [2.79490671e-08, 1.02308121e-07, 1.71204775e-07],
              [2.62092996e-08, 1.02718658e-07, 1.88728990e-07],
              [2.34818103e-08, 1.03739211e-07, 1.98115629e-07],
              [2.53572559e-08, 1.08082645e-07, 2.19582347e-07],
      ...
              [1.05008013e-07, 1.26518316e-07, 2.35757994e-07],
              [1.06341147e-07, 3.14839610e-07, 4.57502239e-07],
              [1.06996765e-07, 3.11565771e-07, 3.97820173e-07],
              [1.10653318e-07, 4.02599085e-07, 3.34023014e-07],
              [1.23020456e-07, 3.97435031e-07, 2.64594632e-07],
              [1.38877965e-07, 3.69533637e-07, 2.31153578e-07],
              [1.55802979e-07, 3.41629004e-07, 1.98067397e-07],
              [1.71049919e-07, 2.96018698e-07, 1.74979576e-07],
              [1.85744952e-07, 2.51837662e-07, 1.61111473e-07],
              [1.85082229e-07, 2.25853171e-07, 1.47434548e-07],
              [1.84550530e-07, 2.08649141e-07, 1.40715855e-07],
              [1.84773924e-07, 1.95753216e-07, 1.38844769e-07],
              [1.71781878e-07, 1.28045869e-07, 9.42439726e-08],
              [2.01993771e-07, 7.92810866e-08, 3.86436980e-08],
              [1.56741407e-07, 1.01999774e-07, 2.64388085e-08],
              [1.30592014e-07, 6.58632686e-08, 2.03692281e-08],
              [1.72341856e-07, 9.93372993e-08, 1.35294016e-08],
              [1.64754084e-07, 1.01589876e-07, 1.64205378e-08],
              [1.52421507e-07, 9.34200841e-08, 1.83120470e-08],
              [1.39953386e-07, 8.80785507e-08, 2.34809825e-08]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      3.153e-08 5.147e-08 ... 5.686e-09

      long_name :

      $χ$

      units :

      C^2/s

      array([[[3.15311368e-08, 5.14684650e-08, 9.00696406e-08],
              [3.15410986e-08, 5.30324087e-08, 9.01792561e-08],
              [3.15075219e-08, 5.37205800e-08, 9.82160913e-08],
              [3.20624061e-08, 5.38295062e-08, 9.03460275e-08],
              [3.18296536e-08, 5.18737444e-08, 9.16030842e-08],
              [3.06467669e-08, 5.12408036e-08, 9.39065075e-08],
              [3.04877297e-08, 4.99954069e-08, 8.79619648e-08],
              [3.04382226e-08, 4.94467329e-08, 9.59539150e-08],
              [3.07606456e-08, 4.88297900e-08, 9.66301599e-08],
              [3.07989403e-08, 4.88755845e-08, 9.55644310e-08],
              [3.07602761e-08, 4.79528133e-08, 9.98426657e-08],
              [3.08518295e-08, 4.82226348e-08, 9.68349596e-08],
              [3.17893125e-08, 4.86282445e-08, 9.36632176e-08],
              [3.16449871e-08, 4.65035299e-08, 9.43617451e-08],
              [3.08822727e-08, 4.60562504e-08, 9.55800061e-08],
              [3.11754427e-08, 4.47915767e-08, 8.63022080e-08],
              [3.06896979e-08, 4.50557103e-08, 8.33114768e-08],
              [3.02294971e-08, 4.45752235e-08, 8.25163937e-08],
              [2.99401677e-08, 4.39887842e-08, 8.17240959e-08],
              [3.01301455e-08, 4.42664927e-08, 8.81232864e-08],
      ...
              [4.69678980e-08, 7.31207379e-08, 1.80065271e-07],
              [4.44437944e-08, 1.60526312e-07, 5.26232157e-07],
              [4.59130831e-08, 2.16911602e-07, 4.20312915e-07],
              [4.35025953e-08, 2.93830965e-07, 3.34603868e-07],
              [4.81800519e-08, 2.92471725e-07, 2.25865008e-07],
              [5.43300516e-08, 2.66074068e-07, 1.74853156e-07],
              [6.02832841e-08, 2.26358566e-07, 1.33450698e-07],
              [6.76159573e-08, 1.83526524e-07, 1.02248393e-07],
              [7.15624537e-08, 1.46292123e-07, 8.52350865e-08],
              [6.93415956e-08, 1.20850444e-07, 7.18475022e-08],
              [6.45878373e-08, 1.03103801e-07, 6.89958028e-08],
              [6.24963974e-08, 8.90549074e-08, 6.63747244e-08],
              [3.71018416e-08, 3.94645703e-08, 4.55000446e-08],
              [6.99339324e-08, 8.64094130e-09, 1.39391068e-08],
              [5.24933412e-08, 9.25000787e-09, 4.61419702e-09],
              [5.24419441e-08, 6.12304518e-09, 5.45677326e-09],
              [1.02914335e-07, 2.33580533e-08, 2.31011832e-09],
              [1.05326329e-07, 3.94027495e-08, 2.59428723e-09],
              [9.97600011e-08, 4.49156161e-08, 3.48193008e-09],
              [8.96089247e-08, 4.73158046e-08, 5.68561020e-09]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      -3.583e-10 -4.566e-10 ... -4.18e-10

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      array([[[-3.58277630e-10, -4.56598898e-10, -6.36808883e-10],
              [-3.61272345e-10, -4.61960303e-10, -6.18004647e-10],
              [-3.62497338e-10, -4.62224758e-10, -6.27580043e-10],
              [-3.65806746e-10, -4.59576710e-10, -6.01129591e-10],
              [-3.65918185e-10, -4.55617932e-10, -6.07656758e-10],
              [-3.62261526e-10, -4.51856774e-10, -6.12711770e-10],
              [-3.61771529e-10, -4.47667459e-10, -6.07582207e-10],
              [-3.55180552e-10, -4.47039850e-10, -6.31304398e-10],
              [-3.58695351e-10, -4.45621262e-10, -6.39133413e-10],
              [-3.58532593e-10, -4.43890119e-10, -6.39311493e-10],
              [-3.59802993e-10, -4.41411629e-10, -6.60119959e-10],
              [-3.58949426e-10, -4.43239168e-10, -6.37624564e-10],
              [-3.60242253e-10, -4.42435810e-10, -6.33906649e-10],
              [-3.61484953e-10, -4.32591463e-10, -6.34564401e-10],
              [-3.57910229e-10, -4.29199953e-10, -6.46178833e-10],
              [-3.57660401e-10, -4.23346747e-10, -6.13737727e-10],
              [-3.55238977e-10, -4.25607383e-10, -5.97280669e-10],
              [-3.52947865e-10, -4.24758007e-10, -5.87040860e-10],
              [-3.49784535e-10, -4.23031971e-10, -5.85980042e-10],
              [-3.53015478e-10, -4.23206359e-10, -6.03543548e-10],
      ...
              [-5.11393239e-09, -1.35741001e-08, -1.60814352e-07],
              [-5.44395062e-09, -2.43575133e-08, -1.45002247e-07],
              [-4.86894791e-09, -4.12963530e-08, -1.47366620e-07],
              [-5.39292611e-09, -5.72370809e-08, -1.25824272e-07],
              [-6.41620401e-09, -6.45678071e-08, -1.10190449e-07],
              [-6.78160905e-09, -6.84167958e-08, -9.38289020e-08],
              [-7.29576932e-09, -6.47739071e-08, -7.68193615e-08],
              [-8.18336510e-09, -5.81149209e-08, -7.04859318e-08],
              [-7.76166331e-09, -5.19686871e-08, -6.43213554e-08],
              [-7.04196568e-09, -4.70384727e-08, -6.16404350e-08],
              [-7.90036125e-09, -4.03301179e-08, -6.00140240e-08],
              [-4.88817342e-09, -1.86461691e-08, -3.97448545e-08],
              [-7.09699721e-09, -1.72511705e-09, -1.00553397e-08],
              [-4.06853129e-09, -1.28853817e-09, -2.58423283e-09],
              [-2.98437519e-09, -1.13034027e-09, -3.60601060e-09],
              [-1.09980309e-08, -2.05348050e-09, -5.57511981e-10],
              [-1.15927854e-08, -4.67873607e-09, -3.97649857e-10],
              [-1.14538530e-08, -5.87054716e-09, -3.38541611e-10],
              [-1.07085452e-08, -6.21498941e-09, -4.18006518e-10]]],
            dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.5587 -0.7393 ... -28.37 -5.81

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[  -0.55872717,   -0.73933429,   -1.01263507],
              [  -0.55909885,   -0.74781458,   -1.02032381],
              [  -0.56339972,   -0.75235563,   -1.04114909],
              [  -0.56574466,   -0.75408049,   -1.00271978],
              [  -0.56327974,   -0.74221028,   -0.9980942 ],
              [  -0.55951453,   -0.73760955,   -1.01441338],
              [  -0.55592129,   -0.73105519,   -0.98191639],
              [  -0.55362725,   -0.72750384,   -1.02130405],
              [  -0.56301865,   -0.7252834 ,   -1.01789328],
              [  -0.55946494,   -0.71693061,   -1.03545581],
              [  -0.55886687,   -0.71391623,   -1.07142686],
              [  -0.55816062,   -0.71663231,   -1.04043361],
              [  -0.56094336,   -0.71781622,   -1.06883406],
              [  -0.55902317,   -0.70272396,   -1.05033251],
              [  -0.55030415,   -0.69910558,   -1.06303512],
              [  -0.55435328,   -0.68816715,   -0.99056348],
              [  -0.54662938,   -0.68960124,   -0.96040917],
              [  -0.5458248 ,   -0.68627143,   -0.95080166],
              [  -0.54284984,   -0.67667759,   -0.94810626],
              [  -0.54519989,   -0.67693639,   -0.979253  ],
      ...
              [ -22.76061424,  -33.70290482,  -94.29752641],
              [ -20.68569739,  -42.29942   , -335.72166172],
              [ -21.75314978,  -72.79134959, -352.68323712],
              [ -20.33633891, -130.71658974, -353.91620765],
              [ -20.80126538, -172.30126404, -320.39020802],
              [ -22.98944112, -190.17481245, -293.028654  ],
              [ -25.53994451, -196.43233475, -267.77275736],
              [ -28.77743047, -187.48167067, -249.95806384],
              [ -32.94550126, -175.95360989, -226.59814176],
              [ -33.82984713, -164.83311868, -216.09387751],
              [ -33.77147363, -156.69712408, -208.27023105],
              [ -35.7552922 , -146.57650858, -204.76277986],
              [ -25.48603855,  -75.301242  , -140.61098295],
              [ -33.06930083,  -12.40822579,  -42.20722391],
              [ -26.257377  ,  -10.45363752,  -15.4468764 ],
              [ -24.46266471,   -5.90238543,  -13.88000668],
              [ -40.2315518 ,  -17.05878402,   -4.50023447],
              [ -42.74567498,  -27.01418861,   -3.93155364],
              [ -40.26200269,  -28.88526491,   -3.4606702 ],
              [ -36.62022982,  -28.36562722,   -5.8097894 ]]])

    • S2
      (depth, hour, tau_bins)
      float32
      0.000118 0.0002866 ... 2.19e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[1.18035634e-04, 2.86617229e-04, 3.68310226e-04],
              [1.19055607e-04, 2.93288234e-04, 3.68738431e-04],
              [1.21223944e-04, 2.90943921e-04, 3.86649830e-04],
              [1.24606799e-04, 2.89873598e-04, 4.03484708e-04],
              [1.22047291e-04, 2.80318171e-04, 4.00653458e-04],
              [1.19750825e-04, 2.80132226e-04, 3.87836946e-04],
              [1.17275791e-04, 2.74971506e-04, 3.79573670e-04],
              [1.19338751e-04, 2.72942707e-04, 3.62854218e-04],
              [1.24937898e-04, 2.71977799e-04, 3.54683434e-04],
              [1.28315500e-04, 2.71855562e-04, 3.48416914e-04],
              [1.28547457e-04, 2.69131095e-04, 3.54306278e-04],
              [1.29285705e-04, 2.68015225e-04, 3.56286910e-04],
              [1.28866639e-04, 2.66737159e-04, 3.46874818e-04],
              [1.24962899e-04, 2.58250860e-04, 3.61833983e-04],
              [1.19088931e-04, 2.55677296e-04, 3.59144819e-04],
              [1.16096089e-04, 2.48583849e-04, 3.58901307e-04],
              [1.14474693e-04, 2.44785100e-04, 3.55322234e-04],
              [1.08996122e-04, 2.44438765e-04, 3.55026539e-04],
              [1.02533719e-04, 2.44985917e-04, 3.44370579e-04],
              [1.07392145e-04, 2.47505552e-04, 3.61505779e-04],
      ...
              [7.28670711e-05, 6.50857328e-05, 6.72916212e-05],
              [7.45321959e-05, 8.67422787e-05, 4.64802906e-05],
              [7.49734172e-05, 9.21883038e-05, 2.75155944e-05],
              [7.82151910e-05, 9.42244806e-05, 1.68252045e-05],
              [8.51124059e-05, 7.84320728e-05, 1.13622445e-05],
              [8.91593227e-05, 6.00015410e-05, 8.76838749e-06],
              [9.46019863e-05, 4.17266747e-05, 6.79051436e-06],
              [9.57649972e-05, 2.93372104e-05, 5.51613903e-06],
              [9.58165911e-05, 2.10807702e-05, 4.71648173e-06],
              [9.64381688e-05, 1.72020154e-05, 4.28609746e-06],
              [9.73792412e-05, 1.46298644e-05, 4.15710019e-06],
              [9.31051254e-05, 1.27367175e-05, 4.07165271e-06],
              [1.01596459e-04, 1.21337544e-05, 3.59626006e-06],
              [1.06722146e-04, 3.36617632e-05, 3.80803567e-06],
              [1.02554128e-04, 4.74796179e-05, 5.74421392e-06],
              [9.62065242e-05, 5.39669963e-05, 6.05823743e-06],
              [9.68023087e-05, 5.40903638e-05, 8.59525062e-06],
              [9.29079615e-05, 5.20505928e-05, 1.37420657e-05],
              [8.81354208e-05, 4.90190614e-05, 1.74025445e-05],
              [8.50617798e-05, 4.72261891e-05, 2.19031808e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002707 0.0002679 ... 1.066e-05

      long_name :

      $N^2$

      units :

      s$^{-2}$

      array([[[2.70653982e-04, 2.67854164e-04, 2.60556408e-04],
              [2.70489603e-04, 2.67931056e-04, 2.61637673e-04],
              [2.68508651e-04, 2.68075935e-04, 2.64729460e-04],
              [2.66491756e-04, 2.67974130e-04, 2.71308294e-04],
              [2.67325930e-04, 2.67938973e-04, 2.70344783e-04],
              [2.69618089e-04, 2.67519441e-04, 2.64699716e-04],
              [2.69445707e-04, 2.67475261e-04, 2.66230258e-04],
              [2.67186144e-04, 2.68850679e-04, 2.58856395e-04],
              [2.71684374e-04, 2.66605173e-04, 2.58436747e-04],
              [2.69634824e-04, 2.68864445e-04, 2.49675912e-04],
              [2.70803575e-04, 2.66544346e-04, 2.50487588e-04],
              [2.71231111e-04, 2.66110990e-04, 2.51161167e-04],
              [2.71857542e-04, 2.66223098e-04, 2.52874015e-04],
              [2.72617326e-04, 2.63849157e-04, 2.66338582e-04],
              [2.71545228e-04, 2.64220784e-04, 2.70796998e-04],
              [2.70907389e-04, 2.63898313e-04, 2.71208119e-04],
              [2.73490848e-04, 2.62059155e-04, 2.72291130e-04],
              [2.73298821e-04, 2.62513553e-04, 2.72275705e-04],
              [2.71756551e-04, 2.64893140e-04, 2.73101410e-04],
              [2.73922691e-04, 2.64201226e-04, 2.70985212e-04],
      ...
              [2.66783991e-05, 2.24435680e-05, 2.01482108e-05],
              [2.76282153e-05, 2.52834870e-05, 1.53130677e-05],
              [2.78609550e-05, 2.53739199e-05, 1.07697870e-05],
              [2.88161682e-05, 2.38241410e-05, 7.61099909e-06],
              [3.00592146e-05, 1.97585177e-05, 5.86610713e-06],
              [3.10059622e-05, 1.59887131e-05, 5.00141596e-06],
              [3.13461569e-05, 1.26825598e-05, 4.25584130e-06],
              [3.19165847e-05, 9.92587957e-06, 3.65537994e-06],
              [3.14851823e-05, 7.92554147e-06, 3.33652520e-06],
              [3.05563590e-05, 6.80374478e-06, 3.12953216e-06],
              [3.05634167e-05, 6.46581339e-06, 3.12648399e-06],
              [3.00894790e-05, 5.96192513e-06, 3.03067145e-06],
              [3.18050443e-05, 5.42599446e-06, 3.01429736e-06],
              [3.27169000e-05, 9.65514482e-06, 2.92601317e-06],
              [3.30679140e-05, 1.31469769e-05, 3.74685192e-06],
              [3.10162686e-05, 1.42103781e-05, 4.23103938e-06],
              [3.14569043e-05, 1.57434079e-05, 5.41504414e-06],
              [3.13854980e-05, 1.62794604e-05, 7.40211590e-06],
              [3.05814792e-05, 1.66734153e-05, 9.08484435e-06],
              [2.97653733e-05, 1.71468819e-05, 1.06635689e-05]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      1.866 0.6854 ... 0.4224 0.5125

      long_name :

      $Ri^g$

      array([[[1.8662848 , 0.68544173, 0.5350752 ],
              [1.8600645 , 0.6764416 , 0.52997637],
              [1.8082513 , 0.6684065 , 0.51994437],
              [1.7514868 , 0.66514564, 0.53821963],
              [1.7742116 , 0.6801465 , 0.5362674 ],
              [1.8050451 , 0.6883631 , 0.5284693 ],
              [1.826543  , 0.6967864 , 0.53718793],
              [1.839041  , 0.69765747, 0.538539  ],
              [1.8039743 , 0.69942325, 0.54343367],
              [1.762919  , 0.7034895 , 0.5462879 ],
              [1.7395374 , 0.71042335, 0.5473652 ],
              [1.743981  , 0.7068553 , 0.5498181 ],
              [1.7485907 , 0.7094732 , 0.5552983 ],
              [1.800962  , 0.7201221 , 0.56303406],
              [1.8770511 , 0.7300272 , 0.5768663 ],
              [1.8958896 , 0.7441837 , 0.59068596],
              [1.9319459 , 0.7465268 , 0.6032988 ],
              [1.9969598 , 0.76601106, 0.59619606],
              [2.1091611 , 0.77056324, 0.59924066],
              [2.072525  , 0.76344836, 0.5768527 ],
      ...
              [0.417879  , 0.3804391 , 0.3238179 ],
              [0.41090554, 0.30941188, 0.3488606 ],
              [0.403327  , 0.30385834, 0.392285  ],
              [0.39265534, 0.28617096, 0.46646762],
              [0.36620998, 0.2877478 , 0.5247332 ],
              [0.34303582, 0.31285378, 0.5649361 ],
              [0.3227075 , 0.34359443, 0.61189747],
              [0.3142567 , 0.38431424, 0.65183944],
              [0.30991292, 0.41828164, 0.6832897 ],
              [0.31734043, 0.44741976, 0.71992517],
              [0.32099736, 0.47542647, 0.747839  ],
              [0.33062443, 0.4994597 , 0.74357045],
              [0.32144618, 0.5118849 , 0.80607224],
              [0.31450492, 0.352032  , 0.76655614],
              [0.33332175, 0.34234598, 0.6389044 ],
              [0.35182685, 0.34710622, 0.6787852 ],
              [0.3533942 , 0.3665613 , 0.63400173],
              [0.36926734, 0.38762894, 0.5510011 ],
              [0.38271207, 0.4089583 , 0.52773184],
              [0.39180535, 0.42236108, 0.5125109 ]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      1.75 0.6778 0.516 ... 0.2708 0.353

      long_name :

      $Ri^g_T$

      array([[[1.7498139 , 0.6778394 , 0.5160031 ],
              [1.7240694 , 0.6695879 , 0.49940598],
              [1.6583761 , 0.6628067 , 0.498627  ],
              [1.6018262 , 0.658349  , 0.51457024],
              [1.6139219 , 0.66992456, 0.52054894],
              [1.6932245 , 0.6747377 , 0.5110692 ],
              [1.7320414 , 0.68135345, 0.5248196 ],
              [1.7112534 , 0.6885524 , 0.52049387],
              [1.6603553 , 0.69147515, 0.5276542 ],
              [1.6072863 , 0.6940439 , 0.542287  ],
              [1.5864432 , 0.70075834, 0.5364443 ],
              [1.586266  , 0.70577735, 0.54150844],
              [1.5927796 , 0.70689046, 0.5504924 ],
              [1.6602877 , 0.7092931 , 0.55996585],
              [1.7805405 , 0.7184469 , 0.5745784 ],
              [1.8147937 , 0.7288473 , 0.58771074],
              [1.8759367 , 0.7344651 , 0.59955335],
              [1.9077746 , 0.7484478 , 0.59346616],
              [1.9881756 , 0.7515857 , 0.5964693 ],
              [1.9673991 , 0.7486092 , 0.56754345],
      ...
              [0.2634116 , 0.2507721 , 0.24641225],
              [0.2607986 , 0.21899809, 0.26634485],
              [0.25649   , 0.2143133 , 0.2940078 ],
              [0.25049907, 0.20534892, 0.32926372],
              [0.23761362, 0.20744732, 0.35544658],
              [0.22342424, 0.21846932, 0.37724414],
              [0.21559256, 0.2321828 , 0.39727533],
              [0.21003583, 0.2461529 , 0.41424218],
              [0.21187592, 0.26617616, 0.4138385 ],
              [0.21543066, 0.27246305, 0.436479  ],
              [0.2142856 , 0.27827302, 0.4584359 ],
              [0.2159077 , 0.29173055, 0.43860143],
              [0.20547742, 0.29568854, 0.4857257 ],
              [0.21092497, 0.21182545, 0.4756171 ],
              [0.22476283, 0.2046566 , 0.37278977],
              [0.23309311, 0.20733707, 0.4022013 ],
              [0.23358554, 0.22945035, 0.39872727],
              [0.24027762, 0.24394931, 0.36718082],
              [0.24860656, 0.26064178, 0.36847693],
              [0.25379637, 0.27075374, 0.35295993]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.0295 0.054 ... 0.05439 0.08311

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.0295027 , 0.05400424, 0.08233272],
             [0.029748  , 0.0539082 , 0.08225691],
             [0.02973902, 0.05381709, 0.08261511],
             [0.02958953, 0.05378674, 0.08290136],
             [0.02952781, 0.0541986 , 0.08320552],
             [0.02957567, 0.05422445, 0.08379569],
             [0.02954055, 0.05467816, 0.08413152],
             [0.02949499, 0.05448272, 0.08375749],
             [0.02946378, 0.05434905, 0.08394575],
             [0.02955062, 0.05434605, 0.08510967],
             [0.02943835, 0.05417863, 0.08425807],
             [0.02927817, 0.05410217, 0.08441167],
             [0.02894278, 0.05420254, 0.08438533],
             [0.02929234, 0.05503666, 0.08478092],
             [0.0293923 , 0.05574505, 0.08499894],
             [0.0296241 , 0.05632416, 0.08501871],
             [0.03005047, 0.05656263, 0.08522117],
             [0.03020069, 0.05679295, 0.08566844],
             [0.03010707, 0.05716396, 0.08595885],
             [0.02992886, 0.05671743, 0.08496676],
             [0.03006576, 0.05620772, 0.08470601],
             [0.02999885, 0.05570246, 0.08516616],
             [0.02975408, 0.05507209, 0.08390015],
             [0.02965912, 0.05439087, 0.0831105 ]], dtype=float32)

  • Attributes: (1)

    title :

    baseline

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006196
      eps       (depth, hour, tau_bins) float32 3.067e-09 6.996e-09 ... 3.577e-08
      chi       (depth, hour, tau_bins) float32 1.417e-08 1.12e-08 ... 1.021e-08
      Jb        (depth, hour, tau_bins) float32 -2.363e-10 ... -8.176e-10
      Jq        (depth, hour, tau_bins) float64 -0.3639 -0.3232 ... -34.57 -7.167
      S2        (depth, hour, tau_bins) float32 1.608e-05 3.475e-05 ... 4.999e-05
      N2        (depth, hour, tau_bins) float32 0.0002237 0.0002014 ... 1.187e-05
      Rig       (depth, hour, tau_bins) float32 13.2 4.297 3.056 ... 0.2232 0.2512
      Rig_T     (depth, hour, tau_bins) float32 13.39 4.173 ... 0.1836 0.2279
      tau       (hour, tau_bins) float32 0.02931 0.05352 0.0824 ... 0.0542 0.08266
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 1.001e-06 ... 0.0006196

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062664e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
      ...
              [8.69949930e-04, 9.59244964e-04, 1.11343141e-03],
              [8.50179931e-04, 1.02828699e-03, 1.24498047e-02],
              [8.22628965e-04, 1.05729094e-03, 3.77871096e-03],
              [8.18862813e-04, 1.26605667e-03, 2.33795643e-02],
              [8.30616336e-04, 1.36252248e-03, 3.00153233e-02],
              [8.26583593e-04, 1.66581431e-03, 3.50915715e-02],
              [8.35646526e-04, 2.89651891e-03, 4.03600074e-02],
              [8.40844936e-04, 5.73793054e-03, 4.29606661e-02],
              [8.83748115e-04, 8.86736996e-03, 4.64188121e-02],
              [8.89282324e-04, 1.00382138e-02, 4.53674309e-02],
              [9.06878675e-04, 1.16374400e-02, 4.41440344e-02],
              [9.52538336e-04, 1.18752746e-02, 4.22920287e-02],
              [9.77167627e-04, 3.11270822e-03, 2.84510609e-02],
              [1.07384217e-03, 1.43216818e-03, 3.58461030e-03],
              [9.83901555e-04, 1.30531006e-03, 1.26952492e-03],
              [9.94352158e-04, 8.17729277e-04, 1.27175078e-03],
              [1.12384127e-03, 1.34078134e-03, 5.45061426e-04],
              [1.12999673e-03, 1.29678287e-03, 6.57544471e-04],
              [1.10153994e-03, 1.23138377e-03, 6.13115670e-04],
              [1.06722605e-03, 1.15680229e-03, 6.19593775e-04]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      3.067e-09 6.996e-09 ... 3.577e-08

      long_name :

      $SP$

      units :

      W/kg

      array([[[3.06701176e-09, 6.99570357e-09, 1.12792096e-08],
              [2.95429015e-09, 7.27236227e-09, 1.16628520e-08],
              [3.03435321e-09, 7.29928473e-09, 1.15865966e-08],
              [3.09861203e-09, 7.32230099e-09, 1.05787965e-08],
              [3.11136494e-09, 7.09674719e-09, 1.22516060e-08],
              [3.04559178e-09, 6.99951208e-09, 1.30705642e-08],
              [2.99432013e-09, 6.73037848e-09, 1.37911904e-08],
              [3.08337444e-09, 6.66707800e-09, 1.50147272e-08],
              [3.12555049e-09, 6.68693634e-09, 1.58194240e-08],
              [3.12062776e-09, 6.52366206e-09, 1.66755285e-08],
              [3.05749825e-09, 6.75726852e-09, 1.61235363e-08],
              [3.13664117e-09, 6.70460842e-09, 1.47471004e-08],
              [3.15217874e-09, 6.90299906e-09, 1.50834563e-08],
              [3.10377768e-09, 6.81277612e-09, 1.38752387e-08],
              [2.94250735e-09, 6.56541577e-09, 1.35846872e-08],
              [2.94795166e-09, 6.39441211e-09, 1.15830145e-08],
              [2.81318369e-09, 6.39819486e-09, 1.10841469e-08],
              [2.77422463e-09, 6.17787199e-09, 1.08094866e-08],
              [2.80508683e-09, 5.86984106e-09, 1.12552065e-08],
              [2.77357826e-09, 6.00636252e-09, 1.14595746e-08],
      ...
              [1.57990542e-07, 1.34023310e-07, 2.11007034e-07],
              [1.52075884e-07, 2.07358511e-07, 8.69410428e-07],
              [1.49262263e-07, 2.32231471e-07, 5.85107387e-07],
              [1.47833191e-07, 3.86520156e-07, 7.40398832e-07],
              [1.56316773e-07, 5.23080075e-07, 5.81518179e-07],
              [1.63320493e-07, 5.83236556e-07, 4.97904125e-07],
              [1.74322750e-07, 6.14305293e-07, 4.01600914e-07],
              [1.87730222e-07, 5.85696284e-07, 3.23046464e-07],
              [2.10503316e-07, 5.40947042e-07, 2.78580956e-07],
              [2.11623188e-07, 4.88402975e-07, 2.46581408e-07],
              [2.17197865e-07, 4.53600649e-07, 2.35122315e-07],
              [2.25392967e-07, 4.29075641e-07, 2.21211138e-07],
              [2.16730115e-07, 3.21438961e-07, 1.52973996e-07],
              [2.28090641e-07, 2.25466991e-07, 6.30578967e-08],
              [1.96342341e-07, 2.48357935e-07, 4.92314278e-08],
              [1.94988189e-07, 1.77086974e-07, 4.40254162e-08],
              [2.22069204e-07, 2.16667615e-07, 2.62699125e-08],
              [2.26215022e-07, 1.92706182e-07, 3.23837241e-08],
              [2.20546781e-07, 1.71657675e-07, 3.60358072e-08],
              [2.06545138e-07, 1.56897627e-07, 3.57718406e-08]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      1.417e-08 1.12e-08 ... 1.021e-08

      long_name :

      $χ$

      units :

      C^2/s

      array([[[1.41654599e-08, 1.12043317e-08, 1.14997345e-08],
              [1.43564414e-08, 1.10966809e-08, 1.06867253e-08],
              [1.40540273e-08, 1.12314247e-08, 1.03123279e-08],
              [1.41355176e-08, 1.13349161e-08, 1.00236583e-08],
              [1.42605980e-08, 1.10975300e-08, 1.07465459e-08],
              [1.42887382e-08, 1.11918066e-08, 1.07850138e-08],
              [1.43708263e-08, 1.12686127e-08, 1.08477582e-08],
              [1.42204337e-08, 1.12907834e-08, 1.09332676e-08],
              [1.43949102e-08, 1.13406422e-08, 1.08743325e-08],
              [1.42460177e-08, 1.14293757e-08, 1.04407292e-08],
              [1.40131853e-08, 1.15438645e-08, 1.04089919e-08],
              [1.38146916e-08, 1.15858061e-08, 1.04847828e-08],
              [1.37048985e-08, 1.15371819e-08, 1.06222746e-08],
              [1.38580338e-08, 1.15255503e-08, 1.07217994e-08],
              [1.39460585e-08, 1.15467724e-08, 1.07971161e-08],
              [1.40707135e-08, 1.14792176e-08, 1.11246070e-08],
              [1.44085774e-08, 1.13738761e-08, 1.11539817e-08],
              [1.43768464e-08, 1.14945884e-08, 1.12381482e-08],
              [1.44178420e-08, 1.15233316e-08, 1.11600116e-08],
              [1.44640957e-08, 1.12931424e-08, 1.13877565e-08],
      ...
              [1.43766911e-07, 1.03006442e-07, 8.78815598e-08],
              [1.37191023e-07, 1.83328794e-07, 7.09568042e-07],
              [1.25446263e-07, 1.67309494e-07, 3.59490400e-07],
              [1.29034063e-07, 2.71363888e-07, 5.55303927e-07],
              [1.28467946e-07, 3.13767430e-07, 4.10859371e-07],
              [1.31779615e-07, 3.50028699e-07, 3.47528783e-07],
              [1.37602086e-07, 3.64599771e-07, 2.63568069e-07],
              [1.47534877e-07, 3.30650295e-07, 2.13382762e-07],
              [1.61122401e-07, 2.85556155e-07, 1.73867647e-07],
              [1.52407182e-07, 2.44686731e-07, 1.56882791e-07],
              [1.51709003e-07, 2.20665001e-07, 1.45941712e-07],
              [1.50763512e-07, 1.98459645e-07, 1.46552651e-07],
              [1.29097174e-07, 8.24987865e-08, 7.70501956e-08],
              [1.57343763e-07, 4.65327865e-08, 1.47697543e-08],
              [1.21017209e-07, 3.88323684e-08, 6.95555213e-09],
              [1.44461794e-07, 2.23305978e-08, 6.47459286e-09],
              [1.90294784e-07, 1.18683403e-07, 3.80536269e-09],
              [2.08375866e-07, 1.24235868e-07, 7.61631114e-09],
              [2.02433256e-07, 1.21694356e-07, 1.00547233e-08],
              [1.91222796e-07, 1.17619521e-07, 1.02139150e-08]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      -2.363e-10 ... -8.176e-10

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[-2.36305003e-10, -2.03665917e-10, -2.08056850e-10],
              [-2.36786424e-10, -2.02221850e-10, -2.09279122e-10],
              [-2.33130792e-10, -2.03391748e-10, -2.06526990e-10],
              [-2.32916963e-10, -2.05656311e-10, -2.00404207e-10],
              [-2.33894487e-10, -2.05576237e-10, -2.02458952e-10],
              [-2.35067965e-10, -2.06578477e-10, -2.01200598e-10],
              [-2.36723557e-10, -2.07841605e-10, -1.97894201e-10],
              [-2.34675418e-10, -2.07456149e-10, -2.04126174e-10],
              [-2.35204634e-10, -2.06371822e-10, -2.01659162e-10],
              [-2.33386255e-10, -2.09510145e-10, -1.95423525e-10],
              [-2.32901309e-10, -2.10196471e-10, -1.95556377e-10],
              [-2.29793545e-10, -2.10664777e-10, -1.96119843e-10],
              [-2.28470382e-10, -2.09507939e-10, -1.99778888e-10],
              [-2.31024630e-10, -2.09797971e-10, -1.99606068e-10],
              [-2.35091085e-10, -2.09259596e-10, -1.99032207e-10],
              [-2.39884834e-10, -2.09699591e-10, -2.02228262e-10],
              [-2.45257092e-10, -2.07174472e-10, -2.03287082e-10],
              [-2.45073906e-10, -2.07818360e-10, -2.04217240e-10],
              [-2.46258847e-10, -2.07750095e-10, -2.05482159e-10],
              [-2.46289100e-10, -2.06136733e-10, -2.08115400e-10],
      ...
              [-1.06592415e-08, -1.59663145e-08, -1.32835780e-07],
              [-1.03445750e-08, -1.54878776e-08, -5.95220513e-08],
              [-1.09041141e-08, -2.38682460e-08, -1.59961715e-07],
              [-1.09902807e-08, -3.03844914e-08, -1.49897033e-07],
              [-1.09048992e-08, -3.81727965e-08, -1.61086376e-07],
              [-1.12891199e-08, -5.17055732e-08, -1.47693413e-07],
              [-1.13750245e-08, -5.63804861e-08, -1.32643848e-07],
              [-1.32148603e-08, -6.07146688e-08, -1.20656594e-07],
              [-1.23768604e-08, -5.72766012e-08, -1.13463010e-07],
              [-1.22800037e-08, -5.40147020e-08, -1.14951519e-07],
              [-1.20845041e-08, -4.98633597e-08, -1.05718762e-07],
              [-9.85280746e-09, -2.33101858e-08, -5.91517733e-08],
              [-1.16605072e-08, -4.99037567e-09, -8.04534306e-09],
              [-7.44766782e-09, -3.38226780e-09, -2.08077888e-09],
              [-8.68693206e-09, -1.48512147e-09, -3.18790239e-09],
              [-1.40054999e-08, -9.78285009e-09, -3.67206598e-10],
              [-1.44412642e-08, -1.08139648e-08, -5.36393763e-10],
              [-1.42424401e-08, -1.11552581e-08, -6.93334057e-10],
              [-1.40376359e-08, -1.11661604e-08, -8.17562462e-10]]],
            dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.3639 -0.3232 ... -34.57 -7.167

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[-3.63856336e-01, -3.23210307e-01, -3.25944868e-01],
              [-3.64410742e-01, -3.22438626e-01, -3.14459372e-01],
              [-3.60407654e-01, -3.23780796e-01, -3.09333925e-01],
              [-3.60840765e-01, -3.25704081e-01, -3.03560250e-01],
              [-3.63860861e-01, -3.23031085e-01, -3.12243092e-01],
              [-3.65510015e-01, -3.23045562e-01, -3.13675629e-01],
              [-3.65869896e-01, -3.24214106e-01, -3.16109657e-01],
              [-3.64828718e-01, -3.25053940e-01, -3.17998895e-01],
              [-3.66923519e-01, -3.24808720e-01, -3.16305131e-01],
              [-3.64535768e-01, -3.27514952e-01, -3.11441584e-01],
              [-3.62548931e-01, -3.28812389e-01, -3.11511710e-01],
              [-3.59663507e-01, -3.29657957e-01, -3.14064940e-01],
              [-3.57138041e-01, -3.28465274e-01, -3.13897933e-01],
              [-3.58997834e-01, -3.27921523e-01, -3.16069328e-01],
              [-3.61749182e-01, -3.27854408e-01, -3.15479682e-01],
              [-3.61282345e-01, -3.26603508e-01, -3.20980527e-01],
              [-3.66450614e-01, -3.26124243e-01, -3.21068923e-01],
              [-3.64773864e-01, -3.27023533e-01, -3.22861464e-01],
              [-3.65693197e-01, -3.27779466e-01, -3.23091044e-01],
              [-3.66243552e-01, -3.25203886e-01, -3.25132215e-01],
      ...
              [-3.11515463e+01, -2.95920814e+01, -3.13240759e+01],
              [-2.97588069e+01, -4.01929936e+01, -3.26898750e+02],
              [-2.80524053e+01, -3.79606483e+01, -1.52565274e+02],
              [-2.87241693e+01, -5.96539726e+01, -3.31737156e+02],
              [-2.92523879e+01, -7.74188050e+01, -3.17598502e+02],
              [-2.97933349e+01, -1.03626681e+02, -3.14021997e+02],
              [-3.06915511e+01, -1.33709094e+02, -2.94227293e+02],
              [-3.16197143e+01, -1.50164779e+02, -2.75888518e+02],
              [-3.48487573e+01, -1.54185040e+02, -2.59916040e+02],
              [-3.53652229e+01, -1.47535313e+02, -2.41424957e+02],
              [-3.63852525e+01, -1.43487593e+02, -2.32847570e+02],
              [-3.87944462e+01, -1.38562537e+02, -2.20087752e+02],
              [-3.47095415e+01, -6.84364904e+01, -1.35048197e+02],
              [-3.79342051e+01, -2.57598461e+01, -2.68607238e+01],
              [-3.00724935e+01, -1.82040015e+01, -1.15180749e+01],
              [-3.35407877e+01, -9.14485579e+00, -9.79402621e+00],
              [-4.07352508e+01, -3.76753676e+01, -3.51846569e+00],
              [-4.24517449e+01, -3.83129382e+01, -5.99288931e+00],
              [-4.14190661e+01, -3.68610034e+01, -7.49174915e+00],
              [-3.99809860e+01, -3.45665587e+01, -7.16703748e+00]]])

    • S2
      (depth, hour, tau_bins)
      float32
      1.608e-05 3.475e-05 ... 4.999e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[1.60773343e-05, 3.47473433e-05, 5.45502517e-05],
              [1.55510897e-05, 3.62657956e-05, 5.80403103e-05],
              [1.57532741e-05, 3.61047132e-05, 5.88809962e-05],
              [1.61362404e-05, 3.65621709e-05, 5.16177024e-05],
              [1.63347595e-05, 3.55188567e-05, 6.21003710e-05],
              [1.59758565e-05, 3.49079783e-05, 6.53139577e-05],
              [1.57272680e-05, 3.38322498e-05, 6.75400515e-05],
              [1.60485488e-05, 3.36952726e-05, 7.21537217e-05],
              [1.64259400e-05, 3.36227749e-05, 6.97661089e-05],
              [1.64107878e-05, 3.27744456e-05, 8.03719886e-05],
              [1.60798663e-05, 3.40344159e-05, 7.83935102e-05],
              [1.63162404e-05, 3.36289268e-05, 7.09879023e-05],
              [1.63955192e-05, 3.41706000e-05, 7.23957492e-05],
              [1.62350207e-05, 3.38442551e-05, 6.78550277e-05],
              [1.52499942e-05, 3.28904680e-05, 6.58997596e-05],
              [1.51459171e-05, 3.21342086e-05, 5.73855250e-05],
              [1.46635284e-05, 3.17903687e-05, 5.46010815e-05],
              [1.46638777e-05, 3.11573203e-05, 5.30258621e-05],
              [1.46625471e-05, 2.97258357e-05, 5.51550838e-05],
              [1.46957009e-05, 3.03876823e-05, 5.71436394e-05],
      ...
              [1.76954345e-04, 1.31229055e-04, 1.19671509e-04],
              [1.75386085e-04, 1.44285470e-04, 9.24286651e-05],
              [1.76493719e-04, 1.65989506e-04, 9.93364883e-05],
              [1.77259906e-04, 1.83417767e-04, 6.67022250e-05],
              [1.80237097e-04, 2.02479947e-04, 4.05998107e-05],
              [1.86967096e-04, 2.01381699e-04, 2.87006660e-05],
              [1.91430430e-04, 1.85397657e-04, 2.00318373e-05],
              [1.97789792e-04, 1.57587536e-04, 1.56843635e-05],
              [2.03315052e-04, 1.19843135e-04, 1.30644439e-05],
              [2.03890755e-04, 9.93930225e-05, 1.25701235e-05],
              [2.09443897e-04, 8.31915386e-05, 1.15447201e-05],
              [2.06869125e-04, 7.98302353e-05, 1.13064698e-05],
              [2.16365268e-04, 1.05084415e-04, 9.60457783e-06],
              [2.22570961e-04, 1.54111607e-04, 1.23858135e-05],
              [2.14604661e-04, 1.80870440e-04, 1.95051653e-05],
              [2.11807986e-04, 1.81155774e-04, 2.01697949e-05],
              [2.13664738e-04, 1.67245424e-04, 2.97912757e-05],
              [2.12474784e-04, 1.55046437e-04, 3.86240499e-05],
              [2.08408193e-04, 1.45126833e-04, 4.46427548e-05],
              [2.01206974e-04, 1.38201241e-04, 4.99901362e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002237 0.0002014 ... 1.187e-05

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      array([[[2.23716459e-04, 2.01369432e-04, 2.07364807e-04],
              [2.24670584e-04, 2.00559298e-04, 2.04587486e-04],
              [2.22044764e-04, 2.02383279e-04, 2.01458635e-04],
              [2.21417606e-04, 2.03395641e-04, 1.95091299e-04],
              [2.21755530e-04, 2.02223877e-04, 2.01670875e-04],
              [2.21897033e-04, 2.02601805e-04, 2.01380739e-04],
              [2.21933631e-04, 2.02978175e-04, 2.01497838e-04],
              [2.20619098e-04, 2.03436794e-04, 2.02596842e-04],
              [2.21749637e-04, 2.02988711e-04, 2.01417221e-04],
              [2.19857873e-04, 2.04759723e-04, 1.99296846e-04],
              [2.19346490e-04, 2.05749850e-04, 1.98707756e-04],
              [2.18359230e-04, 2.06736469e-04, 2.01719973e-04],
              [2.18257366e-04, 2.06634082e-04, 1.97994610e-04],
              [2.19672744e-04, 2.05267948e-04, 2.00647381e-04],
              [2.20484260e-04, 2.05869466e-04, 1.96984562e-04],
              [2.22277275e-04, 2.05256205e-04, 2.00871931e-04],
              [2.22674425e-04, 2.04421536e-04, 2.00914845e-04],
              [2.22843708e-04, 2.05170858e-04, 2.03752279e-04],
              [2.24134274e-04, 2.05697404e-04, 2.03576696e-04],
              [2.23726267e-04, 2.04792828e-04, 2.06141849e-04],
      ...
              [4.44578545e-05, 3.09555980e-05, 2.18656496e-05],
              [4.37544404e-05, 3.26075969e-05, 2.38331431e-05],
              [4.39932483e-05, 3.30363655e-05, 2.08300789e-05],
              [4.41018747e-05, 3.41599880e-05, 1.92035659e-05],
              [4.40224831e-05, 3.48551039e-05, 1.46976718e-05],
              [4.41208649e-05, 3.49741385e-05, 1.14134200e-05],
              [4.50448970e-05, 3.32406053e-05, 8.23586197e-06],
              [4.56341149e-05, 2.86893446e-05, 7.04849163e-06],
              [4.63023462e-05, 2.27672244e-05, 6.17078695e-06],
              [4.63085671e-05, 2.06949808e-05, 5.77945048e-06],
              [4.70361374e-05, 1.78653881e-05, 5.42836233e-06],
              [4.65924677e-05, 1.72670952e-05, 5.22179744e-06],
              [4.66712881e-05, 1.52227567e-05, 4.89958938e-06],
              [4.81022325e-05, 1.52632801e-05, 4.34061121e-06],
              [4.82442083e-05, 2.59471744e-05, 4.41810153e-06],
              [4.77407229e-05, 2.84563885e-05, 5.11112830e-06],
              [4.76316200e-05, 2.91304204e-05, 5.96908694e-06],
              [4.79773953e-05, 2.88553783e-05, 8.46229523e-06],
              [4.80109811e-05, 2.87334278e-05, 1.05174622e-05],
              [4.71629573e-05, 2.89586733e-05, 1.18731123e-05]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      13.2 4.297 3.056 ... 0.2232 0.2512

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      array([[[13.197857  ,  4.2973194 ,  3.0555031 ],
              [13.821407  ,  4.232544  ,  2.5504222 ],
              [13.510264  ,  4.2386236 ,  2.4945803 ],
              [13.20141   ,  4.2471313 ,  2.4653025 ],
              [13.149447  ,  4.319394  ,  2.4220955 ],
              [13.85933   ,  4.4229817 ,  2.3923457 ],
              [14.107593  ,  4.5487447 ,  2.3519185 ],
              [13.647481  ,  4.598955  ,  2.3381877 ],
              [13.462015  ,  4.5880175 ,  2.3576286 ],
              [13.113719  ,  4.6214905 ,  2.182707  ],
              [13.353025  ,  4.4489136 ,  2.2182493 ],
              [13.087497  ,  4.5611644 ,  2.2721744 ],
              [12.944699  ,  4.5314035 ,  2.3206887 ],
              [12.845064  ,  4.613967  ,  2.453578  ],
              [13.476563  ,  4.813492  ,  2.4708343 ],
              [13.658272  ,  5.07368   ,  2.7000542 ],
              [14.541157  ,  5.212734  ,  2.7889817 ],
              [14.787399  ,  5.2871675 ,  2.8942657 ],
              [15.181756  ,  5.5781765 ,  2.8768818 ],
              [15.022156  ,  5.3412776 ,  2.904497  ],
      ...
              [ 0.2605168 ,  0.24626791,  0.2186516 ],
              [ 0.26234898,  0.24100047,  0.27151155],
              [ 0.26460433,  0.22231369,  0.22628295],
              [ 0.26444855,  0.21137132,  0.32527298],
              [ 0.26379338,  0.2065137 ,  0.37389553],
              [ 0.26258442,  0.20844041,  0.3997121 ],
              [ 0.25847906,  0.22196066,  0.4238111 ],
              [ 0.25546002,  0.2310276 ,  0.44557902],
              [ 0.24929616,  0.24503568,  0.47095898],
              [ 0.25088966,  0.25116956,  0.47218096],
              [ 0.25009263,  0.25732547,  0.46698648],
              [ 0.24756661,  0.26599523,  0.46304277],
              [ 0.22898337,  0.22704817,  0.5012692 ],
              [ 0.21460304,  0.16282685,  0.3467214 ],
              [ 0.22194551,  0.16865185,  0.23832142],
              [ 0.223914  ,  0.17884398,  0.23131603],
              [ 0.22466302,  0.19137523,  0.21537249],
              [ 0.22704174,  0.20382346,  0.23264292],
              [ 0.23163387,  0.21370158,  0.24267618],
              [ 0.2359288 ,  0.22320737,  0.25116214]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      13.39 4.173 3.016 ... 0.1836 0.2279

      long_name :

      $Ri^g_T$

      array([[[13.39003   ,  4.1733675 ,  3.0161352 ],
              [14.119032  ,  4.050316  ,  2.7403107 ],
              [13.708002  ,  4.044218  ,  2.772739  ],
              [13.35049   ,  4.09664   ,  2.7337017 ],
              [13.440435  ,  4.2385445 ,  2.5692105 ],
              [13.888355  ,  4.367624  ,  2.4463243 ],
              [14.08926   ,  4.548192  ,  2.2969518 ],
              [13.814713  ,  4.5322876 ,  2.2213016 ],
              [13.61069   ,  4.5070376 ,  2.2090302 ],
              [13.551187  ,  4.5452027 ,  2.0633187 ],
              [13.787437  ,  4.4257965 ,  2.0763931 ],
              [13.39185   ,  4.480337  ,  2.1504233 ],
              [13.147581  ,  4.426073  ,  2.1903646 ],
              [13.178958  ,  4.545905  ,  2.2907367 ],
              [13.68602   ,  4.709573  ,  2.301371  ],
              [13.814043  ,  4.880429  ,  2.4320097 ],
              [14.562265  ,  5.046787  ,  2.570214  ],
              [14.989765  ,  5.1713166 ,  2.7265682 ],
              [15.491265  ,  5.3896675 ,  2.7282755 ],
              [15.379088  ,  5.16234   ,  2.7647042 ],
      ...
              [ 0.19949941,  0.19074827,  0.17702514],
              [ 0.20222469,  0.18197736,  0.18649122],
              [ 0.20144732,  0.16863236,  0.17766082],
              [ 0.20056407,  0.15703726,  0.18814558],
              [ 0.20053883,  0.14603414,  0.20615524],
              [ 0.1938774 ,  0.14094096,  0.23102301],
              [ 0.18646285,  0.14410391,  0.25327593],
              [ 0.1793328 ,  0.14658612,  0.28199226],
              [ 0.16956541,  0.1529384 ,  0.29382858],
              [ 0.16857266,  0.15509175,  0.3094122 ],
              [ 0.16314791,  0.15785547,  0.30625468],
              [ 0.16122043,  0.16049628,  0.3079362 ],
              [ 0.15586448,  0.14820942,  0.3269163 ],
              [ 0.15939882,  0.13319623,  0.2494062 ],
              [ 0.16583118,  0.13827197,  0.19804096],
              [ 0.1705277 ,  0.1412782 ,  0.19478822],
              [ 0.17406169,  0.15591256,  0.21430875],
              [ 0.17862558,  0.16688593,  0.21968636],
              [ 0.1818856 ,  0.17532237,  0.22169115],
              [ 0.18481219,  0.18359019,  0.22792467]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.02931 0.05352 ... 0.0542 0.08266

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.02931377, 0.05352184, 0.08240295],
             [0.02966809, 0.05379798, 0.08218867],
             [0.02963378, 0.0531844 , 0.08235388],
             [0.02950549, 0.05342022, 0.08332323],
             [0.02959662, 0.05367155, 0.08316955],
             [0.0294683 , 0.05404468, 0.08309559],
             [0.02944375, 0.05421706, 0.08337168],
             [0.02952339, 0.05432522, 0.08384206],
             [0.02939557, 0.05427076, 0.08357246],
             [0.02919702, 0.05400485, 0.08424108],
             [0.02917114, 0.05389731, 0.08418184],
             [0.02895542, 0.05386308, 0.08414546],
             [0.02882244, 0.05380915, 0.0840569 ],
             [0.02902017, 0.05480089, 0.08480587],
             [0.02929266, 0.05536883, 0.08480069],
             [0.02949213, 0.05573643, 0.08509558],
             [0.02968458, 0.05615301, 0.08533888],
             [0.02993085, 0.05675283, 0.08584618],
             [0.02995646, 0.05718726, 0.08638418],
             [0.02968866, 0.05656635, 0.08475825],
             [0.0296479 , 0.05585978, 0.08439424],
             [0.02981201, 0.05550362, 0.08500274],
             [0.02963592, 0.0546338 , 0.08361351],
             [0.02963188, 0.05420088, 0.08266187]], dtype=float32)

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 0.000262 ... 0.0003525
      eps       (depth, hour, tau_bins) float32 2.751e-08 1.009e-07 ... 2.409e-08
      chi       (depth, hour, tau_bins) float32 6.284e-08 2.881e-07 ... 6.942e-09
      Jb        (depth, hour, tau_bins) float32 -5.201e-10 ... -8.505e-10
      Jq        (depth, hour, tau_bins) float64 -0.8128 -35.11 ... -31.0 -4.416
      S2        (depth, hour, tau_bins) float32 0.0002157 0.0002126 ... 2.707e-05
      N2        (depth, hour, tau_bins) float32 0.0002498 0.0001501 ... 1.074e-05
      Rig       (depth, hour, tau_bins) float32 0.6921 0.535 ... 0.4047 0.4431
      Rig_T     (depth, hour, tau_bins) float32 0.6525 0.4943 ... 0.3079 0.3627
      tau       (hour, tau_bins) float32 0.02913 0.05397 ... 0.05443 0.08317
  Attributes:
      title:    KD=0, KV=0

  new_baseline.hb

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 0.000262 ... 0.0003525

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[1.00062653e-06, 2.62037240e-04, 1.05890748e-03],
              [1.00062664e-06, 2.72701523e-04, 1.09908008e-03],
              [1.00062664e-06, 2.90863565e-04, 1.02824823e-03],
              [1.00062664e-06, 2.96896702e-04, 1.00071402e-03],
              [1.00062664e-06, 2.66355200e-04, 1.02508185e-03],
              [1.00062653e-06, 2.53402599e-04, 9.46604297e-04],
              [1.00062653e-06, 2.26842472e-04, 8.61362787e-04],
              [1.00062653e-06, 2.20032278e-04, 8.83506727e-04],
              [1.00062664e-06, 2.11917271e-04, 8.30798468e-04],
              [1.00062664e-06, 2.03725998e-04, 8.30619712e-04],
              [1.00062675e-06, 1.92588341e-04, 8.49988079e-04],
              [1.00062675e-06, 1.90688588e-04, 8.54582933e-04],
              [1.00062675e-06, 1.91088926e-04, 7.87007099e-04],
              [1.00062664e-06, 1.57940449e-04, 7.20337499e-04],
              [1.00062653e-06, 1.38884861e-04, 6.53320050e-04],
              [1.00062653e-06, 1.24576676e-04, 6.12310017e-04],
              [1.00062653e-06, 1.00052304e-04, 7.07846077e-04],
              [1.00062641e-06, 9.21100873e-05, 7.37336930e-04],
              [1.00062641e-06, 8.17428736e-05, 7.50365201e-04],
              [1.00062641e-06, 1.24435348e-04, 8.18357163e-04],
      ...
              [1.41367852e-03, 1.79877831e-03, 1.71377081e-02],
              [1.37466111e-03, 2.30275956e-03, 3.32856625e-02],
              [1.34840910e-03, 3.03058885e-03, 4.47302125e-02],
              [1.38495094e-03, 4.78022080e-03, 5.33157960e-02],
              [1.47192599e-03, 9.51274391e-03, 5.98784201e-02],
              [1.55525003e-03, 1.41898468e-02, 6.55668080e-02],
              [1.60700711e-03, 1.95938740e-02, 7.16438368e-02],
              [1.77496276e-03, 2.32385416e-02, 7.43482858e-02],
              [1.91565300e-03, 2.69467719e-02, 7.60267600e-02],
              [1.93555490e-03, 2.79643107e-02, 7.59711117e-02],
              [2.08756095e-03, 2.94796471e-02, 7.63885081e-02],
              [2.20756372e-03, 3.05341445e-02, 7.48512000e-02],
              [2.07593641e-03, 3.59162269e-03, 2.28384528e-02],
              [2.05276557e-03, 1.21652649e-03, 2.78259465e-03],
              [2.36642803e-03, 1.12493162e-03, 2.53933412e-03],
              [2.31716968e-03, 1.69391045e-03, 1.25961495e-03],
              [2.22260458e-03, 1.90275419e-03, 6.10022515e-04],
              [2.06597475e-03, 1.90889882e-03, 6.11530559e-04],
              [1.90941000e-03, 1.74662611e-03, 4.70483530e-04],
              [1.74895767e-03, 1.62311806e-03, 3.52516159e-04]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      2.751e-08 1.009e-07 ... 2.409e-08

      long_name :

      $SP$

      units :

      W/kg

      array([[[2.75141758e-08, 1.00927956e-07, 1.91842332e-07],
              [2.76572063e-08, 1.02083213e-07, 1.81103673e-07],
              [2.84073831e-08, 1.02706849e-07, 1.68768793e-07],
              [2.92201214e-08, 1.00639696e-07, 1.60052934e-07],
              [2.93619955e-08, 9.66570397e-08, 1.54343581e-07],
              [2.81556733e-08, 9.44912699e-08, 1.47770351e-07],
              [2.80691328e-08, 9.18277721e-08, 1.33315652e-07],
              [2.70703797e-08, 8.95299763e-08, 1.31178382e-07],
              [2.80460561e-08, 8.83356108e-08, 1.22648729e-07],
              [2.98051575e-08, 8.39754293e-08, 1.20592745e-07],
              [3.04549310e-08, 8.25981274e-08, 1.13883360e-07],
              [3.01034575e-08, 8.12708763e-08, 1.13250536e-07],
              [3.03417202e-08, 7.92105439e-08, 1.12164003e-07],
              [2.80577197e-08, 7.73159456e-08, 1.08730454e-07],
              [2.65831268e-08, 7.54789724e-08, 1.04768517e-07],
              [2.50681502e-08, 7.38985193e-08, 1.02204666e-07],
              [2.32162165e-08, 7.19843598e-08, 1.22872251e-07],
              [2.16796110e-08, 7.61822463e-08, 1.42449750e-07],
              [1.97460412e-08, 7.91168873e-08, 1.52065113e-07],
              [1.99246042e-08, 8.23590369e-08, 1.69472955e-07],
      ...
              [8.87343390e-08, 1.92970333e-07, 3.78641403e-07],
              [9.27110761e-08, 3.28967673e-07, 4.08734365e-07],
              [9.84670478e-08, 3.96731309e-07, 3.57619427e-07],
              [1.05839803e-07, 4.50980082e-07, 2.93132842e-07],
              [1.18670741e-07, 4.21865536e-07, 2.44616189e-07],
              [1.36195439e-07, 3.84792088e-07, 2.07911881e-07],
              [1.57122187e-07, 3.32016810e-07, 1.79250890e-07],
              [1.79287312e-07, 2.67496006e-07, 1.59683267e-07],
              [1.84715390e-07, 2.25708632e-07, 1.48024810e-07],
              [1.82531465e-07, 2.04892004e-07, 1.40819395e-07],
              [1.78498823e-07, 1.90133846e-07, 1.34555791e-07],
              [1.77062404e-07, 1.72623118e-07, 1.28240089e-07],
              [1.66876461e-07, 7.90816728e-08, 5.31014024e-08],
              [1.57211332e-07, 6.07850836e-08, 1.32667282e-08],
              [1.78480164e-07, 8.18638455e-08, 1.54134518e-08],
              [1.72224148e-07, 9.17385847e-08, 1.27861615e-08],
              [1.62150997e-07, 9.55862518e-08, 1.27261250e-08],
              [1.47029624e-07, 8.91621710e-08, 1.51694479e-08],
              [1.33136325e-07, 8.16399606e-08, 1.85937061e-08],
              [1.20080372e-07, 7.40396757e-08, 2.40899496e-08]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      6.284e-08 2.881e-07 ... 6.942e-09

      long_name :

      $χ$

      units :

      C^2/s

      array([[[6.28427301e-08, 2.88068264e-07, 8.26305950e-07],
              [6.27368166e-08, 2.98960629e-07, 8.71046439e-07],
              [6.44347011e-08, 3.12472736e-07, 9.56297868e-07],
              [6.58234427e-08, 2.90687467e-07, 8.94805169e-07],
              [6.38682138e-08, 2.69036633e-07, 8.48512173e-07],
              [6.23498408e-08, 2.60920700e-07, 7.95632900e-07],
              [6.17878939e-08, 2.35277298e-07, 7.43213093e-07],
              [6.09875315e-08, 2.30552388e-07, 7.15444685e-07],
              [6.19845935e-08, 2.33280574e-07, 6.38697657e-07],
              [6.30164294e-08, 2.23609277e-07, 6.13384714e-07],
              [6.33949782e-08, 2.18304905e-07, 6.51640221e-07],
              [6.44858815e-08, 2.16931255e-07, 6.27009797e-07],
              [6.66231301e-08, 2.17100066e-07, 5.76267610e-07],
              [6.13616891e-08, 2.07990112e-07, 5.76540401e-07],
              [5.89936313e-08, 1.95030083e-07, 5.98523343e-07],
              [5.78152637e-08, 1.80715574e-07, 5.96839300e-07],
              [5.59458790e-08, 1.73955854e-07, 6.28465671e-07],
              [5.44911316e-08, 1.69204824e-07, 6.99216798e-07],
              [5.31692095e-08, 1.68147437e-07, 7.60493265e-07],
              [5.20742489e-08, 1.85152899e-07, 7.71945395e-07],
      ...
              [8.01801932e-08, 1.43546359e-07, 3.93581416e-07],
              [8.15358305e-08, 2.63959834e-07, 4.96796190e-07],
              [8.34458191e-08, 3.63753145e-07, 3.89451685e-07],
              [9.06807145e-08, 3.98819338e-07, 2.63717595e-07],
              [9.65297176e-08, 3.69848379e-07, 1.95779862e-07],
              [1.16798915e-07, 3.11457910e-07, 1.47851480e-07],
              [1.31431392e-07, 2.54156788e-07, 1.17898125e-07],
              [1.45972948e-07, 2.09105082e-07, 9.08319109e-08],
              [1.43596168e-07, 1.64574814e-07, 7.50877760e-08],
              [1.30332225e-07, 1.36982720e-07, 7.11929431e-08],
              [1.25370548e-07, 1.19903476e-07, 6.65046258e-08],
              [1.23980243e-07, 1.06661567e-07, 6.42934452e-08],
              [1.02193404e-07, 1.57689470e-08, 2.12460556e-08],
              [1.21662225e-07, 7.79856713e-09, 2.85935986e-09],
              [1.81704664e-07, 1.52409090e-08, 4.98988229e-09],
              [1.96759984e-07, 4.02906437e-08, 3.78793130e-09],
              [1.93130234e-07, 6.06322743e-08, 3.38508843e-09],
              [1.76792256e-07, 6.97176006e-08, 4.78815654e-09],
              [1.61031181e-07, 6.62385702e-08, 5.76574166e-09],
              [1.42380628e-07, 6.12754292e-08, 6.94171742e-09]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      -5.201e-10 ... -8.505e-10

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[-5.20121723e-10, -9.14844023e-09, -4.93047665e-08],
              [-5.22739962e-10, -9.68542224e-09, -5.20738865e-08],
              [-5.26788724e-10, -1.03196767e-08, -5.22399120e-08],
              [-5.31486521e-10, -9.74224612e-09, -5.16224858e-08],
              [-5.28820432e-10, -8.79323636e-09, -4.80655693e-08],
              [-5.22581700e-10, -7.71664954e-09, -4.50965629e-08],
              [-5.21203691e-10, -5.64962477e-09, -4.01015399e-08],
              [-5.18142307e-10, -5.64664893e-09, -3.91272224e-08],
              [-5.23529831e-10, -5.48414247e-09, -3.74587685e-08],
              [-5.28541544e-10, -5.04714226e-09, -3.46567788e-08],
              [-5.26243826e-10, -4.30064651e-09, -3.66826640e-08],
              [-5.30970545e-10, -4.58714178e-09, -3.40342439e-08],
              [-5.40062717e-10, -4.76399675e-09, -3.22511404e-08],
              [-5.22477839e-10, -3.94990529e-09, -3.11737196e-08],
              [-5.14006226e-10, -2.94821079e-09, -3.12277812e-08],
              [-5.08801501e-10, -2.20570762e-09, -3.06338883e-08],
              [-5.04617959e-10, -1.54705970e-09, -3.28621717e-08],
              [-4.98949326e-10, -1.16123022e-09, -3.47503857e-08],
              [-4.88723784e-10, -1.06063713e-09, -3.58807526e-08],
              [-4.87477392e-10, -2.45552156e-09, -3.57378731e-08],
      ...
              [-1.19211130e-08, -2.91519573e-08, -1.85755169e-07],
              [-1.16928520e-08, -4.86725327e-08, -1.75252964e-07],
              [-1.28964057e-08, -7.18805637e-08, -1.54877227e-07],
              [-1.38640353e-08, -8.52027426e-08, -1.34078221e-07],
              [-1.57190652e-08, -8.47513775e-08, -1.16542580e-07],
              [-1.61323648e-08, -8.39835579e-08, -1.00404989e-07],
              [-1.95944843e-08, -7.88693768e-08, -8.40583709e-08],
              [-1.87968698e-08, -7.23438234e-08, -7.34735437e-08],
              [-1.98578043e-08, -6.28177261e-08, -6.60879067e-08],
              [-1.82452986e-08, -6.05719066e-08, -6.59063346e-08],
              [-2.02872936e-08, -5.50794574e-08, -6.34640145e-08],
              [-1.40410705e-08, -6.71363454e-09, -1.99716030e-08],
              [-1.54548694e-08, -1.65583369e-09, -1.90645566e-09],
              [-2.33801369e-08, -1.92635197e-09, -2.99118907e-09],
              [-2.59499373e-08, -5.00197261e-09, -1.58494307e-09],
              [-2.67266209e-08, -8.41553582e-09, -7.80392917e-10],
              [-2.56543515e-08, -9.63710711e-09, -8.30871316e-10],
              [-2.25431442e-08, -9.36158528e-09, -7.23190507e-10],
              [-2.03528803e-08, -8.93805474e-09, -8.50497450e-10]]],
            dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.8128 -35.11 ... -31.0 -4.416

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[  -0.81275251,  -35.10540508, -107.92516258],
              [  -0.81452745,  -37.30334659, -111.43608918],
              [  -0.81740106,  -38.24310327, -110.81864896],
              [  -0.8337643 ,  -36.90952601, -106.07761669],
              [  -0.83078597,  -33.39633727, -102.65993507],
              [  -0.81278874,  -31.48373516,  -96.20815795],
              [  -0.80897951,  -29.27580381,  -91.31173044],
              [  -0.80735026,  -28.07736327,  -86.57831432],
              [  -0.81581746,  -28.11210656,  -80.24805898],
              [  -0.8243209 ,  -26.31316743,  -79.05538436],
              [  -0.82850863,  -24.78306735,  -78.83334196],
              [  -0.83782546,  -24.51323225,  -76.66792171],
              [  -0.84537657,  -25.62885892,  -73.43640519],
              [  -0.81986974,  -22.42427483,  -71.26076615],
              [  -0.79646231,  -19.5188711 ,  -69.76079995],
              [  -0.78984496,  -16.53241175,  -67.13581603],
              [  -0.77231664,  -13.41119337,  -74.19449757],
              [  -0.7643023 ,  -12.92565907,  -79.32650121],
              [  -0.75232554,  -11.57467935,  -82.422927  ],
              [  -0.7441352 ,  -17.67938361,  -86.26225594],
      ...
              [ -31.87862027,  -44.16697882, -261.96079925],
              [ -31.56887868,  -72.83843194, -377.9896837 ],
              [ -30.9545401 , -128.39360995, -375.99278148],
              [ -31.56165977, -188.84136826, -353.71039164],
              [ -35.07868203, -213.64488296, -324.67284683],
              [ -38.16875777, -216.86511225, -295.0096038 ],
              [ -40.91438287, -214.01366732, -270.30288383],
              [ -47.57095843, -204.76566038, -247.3864047 ],
              [ -51.89456633, -194.27813913, -231.64378862],
              [ -53.58953487, -177.50533146, -221.23058939],
              [ -53.27081906, -171.74451817, -210.98684748],
              [ -57.0413013 , -164.76087878, -205.40908003],
              [ -40.80732238,  -27.55403688,  -71.54460267],
              [ -44.89828358,   -8.20514559,   -9.65387598],
              [ -59.21442771,  -10.90296723,  -11.91643733],
              [ -59.91662008,  -24.30769411,   -7.67025163],
              [ -60.46739161,  -32.15060793,   -4.7937284 ],
              [ -55.39888652,  -34.30766255,   -5.44876747],
              [ -51.89013205,  -32.49015759,   -5.0439275 ],
              [ -46.88159532,  -31.00173399,   -4.41609556]]])

    • S2
      (depth, hour, tau_bins)
      float32
      0.0002157 0.0002126 ... 2.707e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[2.15672277e-04, 2.12571860e-04, 2.09773949e-04],
              [2.11442632e-04, 2.13960826e-04, 2.08297279e-04],
              [2.12287967e-04, 2.13467691e-04, 2.08366328e-04],
              [2.10965911e-04, 2.11972176e-04, 2.07678633e-04],
              [2.12011786e-04, 2.13226915e-04, 2.05537624e-04],
              [2.10400962e-04, 2.13552674e-04, 2.04386102e-04],
              [2.12897561e-04, 2.12111685e-04, 2.02022580e-04],
              [2.10545331e-04, 2.11847306e-04, 1.97839865e-04],
              [2.11565115e-04, 2.10612125e-04, 1.97103727e-04],
              [2.16023400e-04, 2.07201680e-04, 1.95945642e-04],
              [2.16643792e-04, 2.06816840e-04, 1.92040781e-04],
              [2.15596141e-04, 2.06519107e-04, 1.87373429e-04],
              [2.16904009e-04, 2.03615797e-04, 1.89063925e-04],
              [2.13125197e-04, 2.04004653e-04, 1.98501541e-04],
              [2.10339669e-04, 2.04935292e-04, 1.98550842e-04],
              [2.10460654e-04, 2.03726391e-04, 2.05102784e-04],
              [2.10142141e-04, 2.03563191e-04, 2.09907332e-04],
              [2.13177133e-04, 2.04036303e-04, 2.12833940e-04],
              [2.09707534e-04, 2.06934783e-04, 2.16156070e-04],
              [2.09469115e-04, 2.09337260e-04, 2.18377172e-04],
      ...
              [7.56139125e-05, 8.71341690e-05, 4.80451272e-05],
              [7.91544371e-05, 1.01554950e-04, 2.81878711e-05],
              [8.30380959e-05, 1.09442073e-04, 1.88173381e-05],
              [8.69750729e-05, 1.01881058e-04, 1.30142435e-05],
              [9.41247126e-05, 7.86011806e-05, 9.69898065e-06],
              [9.96056042e-05, 5.49317556e-05, 7.44869567e-06],
              [1.06645661e-04, 3.67982102e-05, 5.92534889e-06],
              [1.08007203e-04, 2.58306009e-05, 4.90106504e-06],
              [1.06873224e-04, 1.89379862e-05, 4.29951342e-06],
              [1.06376858e-04, 1.58985531e-05, 4.05997798e-06],
              [1.01732832e-04, 1.36065619e-05, 3.86104784e-06],
              [1.00730147e-04, 1.22374022e-05, 3.82177495e-06],
              [1.14109302e-04, 2.29600610e-05, 3.37873757e-06],
              [1.07913307e-04, 4.26053302e-05, 4.52991026e-06],
              [1.08090324e-04, 5.52821584e-05, 5.62377090e-06],
              [1.04775114e-04, 5.55655897e-05, 7.24206393e-06],
              [1.01420417e-04, 5.40483234e-05, 1.19902697e-05],
              [9.72153139e-05, 5.22391856e-05, 1.74126035e-05],
              [9.16627687e-05, 5.07932564e-05, 2.12584673e-05],
              [8.66877963e-05, 5.03534393e-05, 2.70670953e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002498 0.0001501 ... 1.074e-05

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      array([[[2.49770761e-04, 1.50098756e-04, 1.11120258e-04],
              [2.47220974e-04, 1.48095016e-04, 1.09469358e-04],
              [2.45606410e-04, 1.47212602e-04, 1.16273659e-04],
              [2.44910567e-04, 1.46733626e-04, 1.18762495e-04],
              [2.46512733e-04, 1.49197745e-04, 1.12651309e-04],
              [2.48484663e-04, 1.50325548e-04, 1.12898553e-04],
              [2.50331883e-04, 1.52102992e-04, 1.12781214e-04],
              [2.47574528e-04, 1.54107634e-04, 1.07344546e-04],
              [2.44933646e-04, 1.54900685e-04, 1.09973000e-04],
              [2.42529481e-04, 1.55066184e-04, 1.11506415e-04],
              [2.43119634e-04, 1.55268455e-04, 1.09090055e-04],
              [2.40484907e-04, 1.56507798e-04, 1.07217173e-04],
              [2.42932118e-04, 1.54143461e-04, 1.09352390e-04],
              [2.47160351e-04, 1.55096990e-04, 1.11676884e-04],
              [2.48186872e-04, 1.56149719e-04, 1.17096948e-04],
              [2.47517542e-04, 1.57528339e-04, 1.21044686e-04],
              [2.51487305e-04, 1.58637442e-04, 1.20155579e-04],
              [2.54361890e-04, 1.61505086e-04, 1.20364450e-04],
              [2.58087355e-04, 1.62304452e-04, 1.22957426e-04],
              [2.59101274e-04, 1.60314288e-04, 1.22088502e-04],
      ...
              [2.85044662e-05, 2.38834564e-05, 1.88644735e-05],
              [2.91151373e-05, 2.64380724e-05, 1.56137939e-05],
              [2.97974948e-05, 2.77846120e-05, 1.16638857e-05],
              [3.07708033e-05, 2.75006969e-05, 8.30140198e-06],
              [3.24869034e-05, 2.40745630e-05, 6.47047591e-06],
              [3.33005883e-05, 1.96775745e-05, 5.33579077e-06],
              [3.45363878e-05, 1.50565565e-05, 4.39064070e-06],
              [3.53112300e-05, 1.14167287e-05, 3.83091219e-06],
              [3.45137305e-05, 8.98336566e-06, 3.45510307e-06],
              [3.41937484e-05, 7.86413875e-06, 3.39021835e-06],
              [3.30625990e-05, 7.16474597e-06, 3.19372793e-06],
              [3.16721853e-05, 6.60015530e-06, 3.07497498e-06],
              [3.25626825e-05, 6.08912433e-06, 2.91151355e-06],
              [3.47301902e-05, 7.46536080e-06, 2.89518221e-06],
              [3.47736095e-05, 1.20941550e-05, 3.61292769e-06],
              [3.44612490e-05, 1.47121618e-05, 4.20345714e-06],
              [3.39114413e-05, 1.61574008e-05, 5.35977597e-06],
              [3.25952788e-05, 1.72388645e-05, 7.28179202e-06],
              [3.15559228e-05, 1.78157061e-05, 8.98511098e-06],
              [3.07502341e-05, 1.84755536e-05, 1.07377618e-05]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      0.6921 0.535 ... 0.4047 0.4431

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      array([[[0.6920738 , 0.5349543 , 0.4580132 ],
              [0.6879399 , 0.5314694 , 0.4592346 ],
              [0.6806804 , 0.5308178 , 0.4657764 ],
              [0.6791985 , 0.53030324, 0.47593078],
              [0.6806549 , 0.5339809 , 0.4767241 ],
              [0.68695664, 0.53564453, 0.48169115],
              [0.6896516 , 0.53775823, 0.49365795],
              [0.6888744 , 0.5401193 , 0.49436718],
              [0.68251336, 0.5424917 , 0.5011655 ],
              [0.68021417, 0.54622084, 0.5020577 ],
              [0.6766766 , 0.5488351 , 0.5055933 ],
              [0.6775784 , 0.55120665, 0.5052763 ],
              [0.6728198 , 0.55457515, 0.5086805 ],
              [0.6839738 , 0.55721325, 0.51228535],
              [0.691461  , 0.5597189 , 0.5188104 ],
              [0.69568074, 0.56400734, 0.52135473],
              [0.7045409 , 0.56998813, 0.5013685 ],
              [0.7121001 , 0.56713057, 0.49566406],
              [0.72767735, 0.56552804, 0.490233  ],
              [0.7288958 , 0.5595642 , 0.47649854],
      ...
              [0.40634552, 0.33188626, 0.5003445 ],
              [0.39810854, 0.3208425 , 0.581831  ],
              [0.39363727, 0.32485807, 0.6355092 ],
              [0.38015234, 0.34808362, 0.66806245],
              [0.36744684, 0.39145422, 0.6928216 ],
              [0.35197896, 0.42392755, 0.7202125 ],
              [0.34439027, 0.46392506, 0.74636894],
              [0.34399074, 0.4974593 , 0.77452666],
              [0.3449704 , 0.5331218 , 0.7831469 ],
              [0.34179208, 0.54510725, 0.80061936],
              [0.34201652, 0.5604679 , 0.8105681 ],
              [0.34889582, 0.5762068 , 0.81085813],
              [0.2864733 , 0.38954324, 0.84244406],
              [0.31387162, 0.28162244, 0.6502222 ],
              [0.32518497, 0.30333692, 0.62802637],
              [0.3409173 , 0.334327  , 0.56850284],
              [0.3572793 , 0.35580266, 0.46713182],
              [0.36969233, 0.37518197, 0.453887  ],
              [0.37960896, 0.39090776, 0.44790077],
              [0.39049977, 0.40465185, 0.44312316]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      0.6525 0.4943 ... 0.3079 0.3627

      long_name :

      $Ri^g_T$

      array([[[0.6524795 , 0.49431792, 0.40401042],
              [0.6476281 , 0.49186328, 0.40452516],
              [0.6441446 , 0.4895203 , 0.4143563 ],
              [0.64207554, 0.49051276, 0.41974872],
              [0.6392752 , 0.49562863, 0.4259835 ],
              [0.6485602 , 0.49845082, 0.43141162],
              [0.65098137, 0.50073636, 0.43876797],
              [0.6474688 , 0.50299937, 0.4344629 ],
              [0.64145947, 0.50219667, 0.44302076],
              [0.63325673, 0.5045558 , 0.44308442],
              [0.6337309 , 0.50653815, 0.44803062],
              [0.63019466, 0.5095291 , 0.45472556],
              [0.630605  , 0.50949144, 0.4569693 ],
              [0.6359448 , 0.51431185, 0.46192995],
              [0.64430714, 0.5192264 , 0.472704  ],
              [0.65253395, 0.52388585, 0.47870392],
              [0.6600066 , 0.52851456, 0.4612955 ],
              [0.6755862 , 0.5250453 , 0.45920655],
              [0.68343335, 0.52443343, 0.45433536],
              [0.6843606 , 0.52000076, 0.43686157],
      ...
              [0.29926416, 0.23857142, 0.26230258],
              [0.2974115 , 0.21804348, 0.30031466],
              [0.28810856, 0.21146052, 0.32518813],
              [0.28323996, 0.21127589, 0.3577328 ],
              [0.26677144, 0.2186438 , 0.38158107],
              [0.2535754 , 0.23513977, 0.39806974],
              [0.24205409, 0.25546497, 0.40978163],
              [0.2332109 , 0.2754921 , 0.42806375],
              [0.22958612, 0.2987565 , 0.43636626],
              [0.23360637, 0.306973  , 0.4462604 ],
              [0.23348331, 0.31307265, 0.45968074],
              [0.23676656, 0.32316506, 0.46267766],
              [0.22447279, 0.25387365, 0.5430862 ],
              [0.24416187, 0.2449134 , 0.4584749 ],
              [0.2532297 , 0.24663666, 0.46583867],
              [0.26291543, 0.25914267, 0.46689865],
              [0.27117586, 0.27355063, 0.43329036],
              [0.28200358, 0.28652632, 0.39690372],
              [0.28587258, 0.29844803, 0.38097277],
              [0.29391518, 0.30785322, 0.36271706]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.02913 0.05397 ... 0.05443 0.08317

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.02912814, 0.05397057, 0.08243489],
             [0.02951978, 0.05395206, 0.08258021],
             [0.02955842, 0.0539168 , 0.08289316],
             [0.02935282, 0.05389314, 0.08354414],
             [0.02921309, 0.05400709, 0.083749  ],
             [0.02929978, 0.05425711, 0.0844179 ],
             [0.02939584, 0.05473844, 0.08448129],
             [0.02932024, 0.05432779, 0.08426537],
             [0.02936672, 0.05443417, 0.08411882],
             [0.02933902, 0.05419946, 0.08512807],
             [0.0292527 , 0.05430375, 0.08504955],
             [0.02921882, 0.05419347, 0.08442991],
             [0.02883983, 0.05426022, 0.08442935],
             [0.02913588, 0.05487758, 0.08464634],
             [0.02929619, 0.05555559, 0.08535865],
             [0.02973468, 0.0561522 , 0.08564808],
             [0.02978581, 0.05643295, 0.08602639],
             [0.0299926 , 0.05669548, 0.08611111],
             [0.0300919 , 0.05693631, 0.08645762],
             [0.02996605, 0.05639978, 0.08547585],
             [0.02971533, 0.05582306, 0.08512087],
             [0.02977535, 0.05557946, 0.08559605],
             [0.0296651 , 0.05494839, 0.08416149],
             [0.02947801, 0.05443047, 0.08317043]], dtype=float32)

  • Attributes: (1)

    title :

    KD=0, KV=0

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0001793
      eps       (depth, hour, tau_bins) float32 1.167e-10 1.08e-09 ... 1.188e-08
      chi       (depth, hour, tau_bins) float32 1.739e-08 2.265e-08 ... 2.921e-09
      Jb        (depth, hour, tau_bins) float32 -2.555e-10 ... -2.291e-11
      Jq        (depth, hour, tau_bins) float64 -0.4038 -0.4617 ... -30.73 -1.899
      S2        (depth, hour, tau_bins) float32 6.214e-05 0.0002147 ... 3.608e-05
      N2        (depth, hour, tau_bins) float32 0.0002396 0.0002716 ... 1.035e-05
      Rig       (depth, hour, tau_bins) float32 3.263 1.133 ... 0.2238 0.2969
      Rig_T     (depth, hour, tau_bins) float32 3.261 1.081 ... 0.1893 0.283
      tau       (hour, tau_bins) float32 0.02975 0.05414 ... 0.05437 0.08353
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  new_baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 1.001e-06 ... 0.0001793

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006263e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
      ...
              [9.4132446e-04, 1.0125230e-03, 3.3146515e-03],
              [9.1822050e-04, 1.1766625e-03, 1.4005989e-02],
              [9.2203892e-04, 1.2403573e-03, 2.3555636e-02],
              [9.2468399e-04, 1.4708596e-03, 3.6420863e-02],
              [9.4251416e-04, 1.7338136e-03, 4.3797132e-02],
              [9.6162333e-04, 3.1932166e-03, 4.8042994e-02],
              [9.9941925e-04, 6.7671593e-03, 5.3763784e-02],
              [1.0284228e-03, 1.0356332e-02, 5.6218788e-02],
              [1.0461721e-03, 1.3733745e-02, 5.8365785e-02],
              [1.0795848e-03, 1.4616154e-02, 5.8618966e-02],
              [1.1059251e-03, 1.5877362e-02, 5.8043797e-02],
              [1.1142871e-03, 1.6551722e-02, 5.5786945e-02],
              [1.2179909e-03, 1.7418606e-03, 1.4232323e-02],
              [1.2699188e-03, 1.2363050e-03, 6.4157473e-04],
              [1.3006497e-03, 1.2935506e-03, 7.0208183e-04],
              [1.2927618e-03, 1.3108684e-03, 2.6574192e-04],
              [1.2749400e-03, 1.2415671e-03, 2.4541048e-04],
              [1.2317502e-03, 1.1815999e-03, 2.0568147e-04],
              [1.1754853e-03, 1.1246946e-03, 2.0106597e-04],
              [1.1240497e-03, 1.0682463e-03, 1.7925282e-04]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      1.167e-10 1.08e-09 ... 1.188e-08

      long_name :

      $SP$

      units :

      W/kg

      array([[[1.16701815e-10, 1.08015230e-09, 5.63116132e-09],
              [1.19111165e-10, 1.07898579e-09, 7.99824651e-09],
              [1.18361237e-10, 1.08791587e-09, 1.18005001e-08],
              [1.27412164e-10, 1.05052633e-09, 1.31663205e-08],
              [1.17635207e-10, 1.03097231e-09, 1.75062738e-08],
              [9.73851971e-11, 1.02187192e-09, 1.88077038e-08],
              [8.75817960e-11, 9.82036785e-10, 1.35005802e-08],
              [9.26381610e-11, 9.65200475e-10, 2.44190765e-08],
              [9.95368094e-11, 9.58345070e-10, 2.83898807e-08],
              [1.10836319e-10, 9.62123381e-10, 2.31838033e-08],
              [1.10051565e-10, 9.44794021e-10, 2.89860242e-08],
              [1.05334921e-10, 9.44809120e-10, 2.77833383e-08],
              [1.03289294e-10, 9.67400937e-10, 1.99497752e-08],
              [9.65807712e-11, 9.38989664e-10, 1.21494139e-08],
              [9.11579978e-11, 8.93369712e-10, 1.11879901e-08],
              [7.61567059e-11, 8.27433844e-10, 6.17140605e-09],
              [6.14191406e-11, 7.72974462e-10, 7.20225124e-09],
              [5.00269270e-11, 7.47057416e-10, 8.19066770e-09],
              [4.44424011e-11, 7.16618154e-10, 5.89383209e-09],
              [4.85056093e-11, 7.63983099e-10, 1.02767244e-08],
      ...
              [1.28741050e-07, 1.87361209e-07, 6.15806414e-07],
              [1.28145004e-07, 2.21985459e-07, 6.48607397e-07],
              [1.27098886e-07, 3.49445969e-07, 6.69406347e-07],
              [1.32843809e-07, 4.85158864e-07, 5.94786627e-07],
              [1.43734553e-07, 5.69935992e-07, 4.71317179e-07],
              [1.57610160e-07, 5.92521985e-07, 4.12530767e-07],
              [1.75905740e-07, 5.66274707e-07, 3.34720511e-07],
              [2.00085310e-07, 5.31975843e-07, 2.84828332e-07],
              [2.20968275e-07, 4.93082723e-07, 2.51338349e-07],
              [2.30489633e-07, 4.53310889e-07, 2.24856095e-07],
              [2.29500998e-07, 4.13383020e-07, 2.13616687e-07],
              [2.36499659e-07, 3.86044064e-07, 2.03071892e-07],
              [2.24949005e-07, 1.76399539e-07, 8.07398237e-08],
              [2.27280538e-07, 1.37437539e-07, 1.52178323e-08],
              [2.22406385e-07, 1.60410394e-07, 2.28007764e-08],
              [2.22824241e-07, 1.65306176e-07, 1.45026267e-08],
              [2.12226539e-07, 1.50751220e-07, 1.26914417e-08],
              [1.97863045e-07, 1.31234714e-07, 1.23363186e-08],
              [1.87246343e-07, 1.16189248e-07, 1.27463036e-08],
              [1.74075794e-07, 1.08521846e-07, 1.18753531e-08]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      1.739e-08 2.265e-08 ... 2.921e-09

      long_name :

      $χ$

      units :

      C^2/s

      array([[[1.73884356e-08, 2.26525483e-08, 2.44861411e-08],
              [1.74541945e-08, 2.25107541e-08, 2.62088093e-08],
              [1.74165553e-08, 2.30953781e-08, 2.58756945e-08],
              [1.76193122e-08, 2.29807107e-08, 2.62177746e-08],
              [1.75198185e-08, 2.28707417e-08, 2.60189026e-08],
              [1.77797759e-08, 2.27358683e-08, 2.63329873e-08],
              [1.77949939e-08, 2.27717489e-08, 2.54955133e-08],
              [1.77553900e-08, 2.28247217e-08, 2.53485677e-08],
              [1.76991506e-08, 2.27228103e-08, 2.53738861e-08],
              [1.76896275e-08, 2.26282602e-08, 2.46510261e-08],
              [1.72673751e-08, 2.28223289e-08, 2.49068179e-08],
              [1.70976175e-08, 2.28065726e-08, 2.59466173e-08],
              [1.74429111e-08, 2.25265229e-08, 2.53189221e-08],
              [1.71922050e-08, 2.24622845e-08, 2.49400109e-08],
              [1.72470962e-08, 2.21477912e-08, 2.48894487e-08],
              [1.71766423e-08, 2.19773977e-08, 2.52571652e-08],
              [1.71460073e-08, 2.18138965e-08, 2.48438727e-08],
              [1.68117325e-08, 2.15464286e-08, 2.56811177e-08],
              [1.70101799e-08, 2.12656666e-08, 2.53530850e-08],
              [1.67263465e-08, 2.17167706e-08, 2.57135699e-08],
      ...
              [1.75568431e-07, 1.69639179e-07, 3.88403151e-07],
              [1.75191545e-07, 1.87313177e-07, 5.02682212e-07],
              [1.78678505e-07, 2.76781094e-07, 4.66718802e-07],
              [1.79366026e-07, 3.55949112e-07, 4.29998522e-07],
              [1.93214674e-07, 4.01829709e-07, 3.54981921e-07],
              [2.06669668e-07, 3.73105195e-07, 2.72762577e-07],
              [2.30700508e-07, 3.66185759e-07, 2.02141905e-07],
              [2.37789180e-07, 3.12547570e-07, 1.59825987e-07],
              [2.32979346e-07, 2.58506304e-07, 1.35901701e-07],
              [2.31390715e-07, 2.30672811e-07, 1.19482976e-07],
              [2.24134709e-07, 2.02506129e-07, 1.22481609e-07],
              [2.03919456e-07, 1.81497555e-07, 1.22820964e-07],
              [2.27858976e-07, 2.54538524e-08, 2.69130194e-08],
              [2.34801988e-07, 3.19046833e-08, 1.72959358e-09],
              [2.60971547e-07, 7.96455524e-08, 3.60839358e-09],
              [2.78977524e-07, 9.71027134e-08, 2.21884200e-09],
              [2.79261258e-07, 9.98343097e-08, 2.69382872e-09],
              [2.76931189e-07, 1.00610364e-07, 2.64924727e-09],
              [2.61743025e-07, 9.72737908e-08, 2.82410162e-09],
              [2.45267103e-07, 9.24204926e-08, 2.92052182e-09]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      -2.555e-10 ... -2.291e-11

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[-2.55470034e-10, -2.91401792e-10, -3.19249349e-10],
              [-2.56496713e-10, -2.91035779e-10, -3.23981342e-10],
              [-2.53829818e-10, -2.94654967e-10, -3.14947873e-10],
              [-2.55533705e-10, -2.92805835e-10, -3.09960974e-10],
              [-2.56380861e-10, -2.91983326e-10, -3.10544312e-10],
              [-2.56754840e-10, -2.91529800e-10, -3.07330911e-10],
              [-2.56076688e-10, -2.91074637e-10, -3.00592301e-10],
              [-2.54780752e-10, -2.91799696e-10, -3.05651116e-10],
              [-2.57439070e-10, -2.91535185e-10, -3.07487813e-10],
              [-2.56943383e-10, -2.93832736e-10, -3.00166780e-10],
              [-2.55828830e-10, -2.93334773e-10, -3.05036496e-10],
              [-2.54726990e-10, -2.92834201e-10, -3.05739545e-10],
              [-2.56902666e-10, -2.92254110e-10, -3.07121273e-10],
              [-2.54530674e-10, -2.91644903e-10, -2.97666614e-10],
              [-2.55004962e-10, -2.89583413e-10, -3.04066772e-10],
              [-2.52027094e-10, -2.88339130e-10, -3.08847586e-10],
              [-2.50817089e-10, -2.87278257e-10, -3.02292469e-10],
              [-2.49159637e-10, -2.86964563e-10, -3.07309900e-10],
              [-2.51137861e-10, -2.85814983e-10, -3.09010206e-10],
              [-2.47247639e-10, -2.86292767e-10, -3.14035603e-10],
      ...
              [-1.65704819e-08, -1.85746529e-08, -1.38406989e-07],
              [-1.66631597e-08, -2.50191388e-08, -1.54171133e-07],
              [-1.78619324e-08, -3.59505563e-08, -1.83110956e-07],
              [-1.81895565e-08, -5.13915772e-08, -1.60862925e-07],
              [-1.90937630e-08, -6.16821083e-08, -1.43145357e-07],
              [-2.18083027e-08, -7.16589668e-08, -1.28102954e-07],
              [-2.29776802e-08, -7.47350626e-08, -1.13467706e-07],
              [-2.24974031e-08, -7.14504864e-08, -1.11496405e-07],
              [-2.32104789e-08, -6.69645033e-08, -1.04462508e-07],
              [-2.28019950e-08, -6.31832933e-08, -1.03584043e-07],
              [-2.19223555e-08, -6.01868848e-08, -1.01363128e-07],
              [-2.10841904e-08, -9.55064028e-09, -2.09327062e-08],
              [-2.02724735e-08, -3.44046680e-09, -6.70337619e-10],
              [-2.20876579e-08, -6.45958576e-09, -1.19146870e-09],
              [-2.26566019e-08, -9.40468858e-09, -4.07481243e-10],
              [-2.29477024e-08, -9.73539294e-09, -2.40757192e-10],
              [-2.31211477e-08, -9.75570824e-09, -1.00751248e-10],
              [-2.25524239e-08, -9.54282964e-09, -8.35792408e-11],
              [-2.11913314e-08, -9.55753965e-09, -2.29118079e-11]]],
            dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.4038 -0.4617 ... -30.73 -1.899

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[  -0.40383201,   -0.46170693,   -0.47979607],
              [  -0.40629385,   -0.46081924,   -0.49478992],
              [  -0.40548916,   -0.46611381,   -0.48753303],
              [  -0.40707212,   -0.46448855,   -0.49537153],
              [  -0.40654759,   -0.46543408,   -0.49638149],
              [  -0.40767024,   -0.46317764,   -0.5001483 ],
              [  -0.40825707,   -0.46347099,   -0.48866422],
              [  -0.40965568,   -0.46438217,   -0.48843255],
              [  -0.40948291,   -0.46580355,   -0.48770104],
              [  -0.40507042,   -0.46631911,   -0.48339054],
              [  -0.40664369,   -0.46244279,   -0.48619947],
              [  -0.39999109,   -0.46313942,   -0.49600014],
              [  -0.40572711,   -0.46118047,   -0.48885789],
              [  -0.40192261,   -0.46193756,   -0.47936888],
              [  -0.40096163,   -0.45710388,   -0.48437711],
              [  -0.39976277,   -0.45286841,   -0.4927702 ],
              [  -0.40124605,   -0.452229  ,   -0.48249731],
              [  -0.39830072,   -0.45048057,   -0.49243466],
              [  -0.40160454,   -0.44609005,   -0.4880124 ],
              [  -0.39683667,   -0.45253467,   -0.49126503],
      ...
              [ -38.01416094,  -37.71273213, -146.00726222],
              [ -38.08362599,  -42.7750946 , -298.59354522],
              [ -37.79889323,  -56.78291587, -339.93163012],
              [ -38.22386721,  -86.86501178, -375.49648732],
              [ -39.24619021, -127.04311749, -336.79308851],
              [ -41.1660171 , -157.00324937, -323.79881367],
              [ -44.60286042, -176.94763065, -294.54638861],
              [ -48.23935169, -180.63089509, -263.29843897],
              [ -49.37562729, -176.74255824, -248.19471826],
              [ -52.69931838, -165.18010348, -235.23162031],
              [ -53.43977151, -160.63160397, -234.23988518],
              [ -53.31057096, -152.06274918, -227.99312568],
              [ -49.36203681,  -33.31373377,  -62.13097584],
              [ -50.61750746,  -17.60716132,   -4.09462887],
              [ -53.39291811,  -30.18945973,   -5.85316103],
              [ -55.27374453,  -34.94254046,   -2.91233767],
              [ -55.69251915,  -34.25285055,   -2.41855112],
              [ -54.34907925,  -32.83374329,   -2.14437555],
              [ -52.16474975,  -31.96696915,   -2.31426363],
              [ -48.46496735,  -30.72726698,   -1.89948483]]])

    • S2
      (depth, hour, tau_bins)
      float32
      6.214e-05 0.0002147 ... 3.608e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[6.2135194e-05, 2.1472209e-04, 3.2808020e-04],
              [6.2408239e-05, 2.1617881e-04, 3.8527348e-04],
              [6.0956540e-05, 2.2225048e-04, 3.8728234e-04],
              [6.1642189e-05, 2.1964675e-04, 4.0369909e-04],
              [5.8844664e-05, 2.1374767e-04, 4.1417207e-04],
              [5.7150966e-05, 2.0920373e-04, 4.1820243e-04],
              [5.5285251e-05, 2.0723182e-04, 4.0379536e-04],
              [5.5278397e-05, 2.0315332e-04, 4.3007903e-04],
              [5.6005490e-05, 2.0021363e-04, 4.3119671e-04],
              [5.7905778e-05, 2.0268056e-04, 4.2984835e-04],
              [5.8969716e-05, 2.0108584e-04, 4.9466669e-04],
              [5.7197434e-05, 2.0400004e-04, 4.8310720e-04],
              [5.8316582e-05, 2.0677842e-04, 4.4798007e-04],
              [5.5217875e-05, 2.0273292e-04, 3.9979507e-04],
              [5.2473857e-05, 1.9077447e-04, 3.7621343e-04],
              [5.0210474e-05, 1.8508767e-04, 3.5905137e-04],
              [4.9514329e-05, 1.7809295e-04, 3.6660919e-04],
              [4.7522713e-05, 1.7627905e-04, 3.5958833e-04],
              [4.7432528e-05, 1.7114768e-04, 3.4196809e-04],
              [4.7061854e-05, 1.7858839e-04, 3.7684879e-04],
      ...
              [1.6946094e-04, 1.5064844e-04, 9.2533010e-05],
              [1.7211071e-04, 1.6139646e-04, 8.4166153e-05],
              [1.7541717e-04, 1.7660802e-04, 6.0221559e-05],
              [1.7440240e-04, 1.9544957e-04, 3.2458531e-05],
              [1.8071404e-04, 1.9552017e-04, 2.2991680e-05],
              [1.8980163e-04, 1.7540010e-04, 1.7840273e-05],
              [1.9475482e-04, 1.4813445e-04, 1.3792065e-05],
              [2.0412008e-04, 1.0925530e-04, 1.1346765e-05],
              [2.0858954e-04, 7.4563985e-05, 9.1552747e-06],
              [2.0937597e-04, 6.3926171e-05, 8.1477992e-06],
              [2.0614298e-04, 5.4879973e-05, 7.9349493e-06],
              [2.0774821e-04, 4.9834987e-05, 7.8745115e-06],
              [2.2729186e-04, 1.0725283e-04, 7.3192496e-06],
              [2.2412286e-04, 1.3586249e-04, 1.1071936e-05],
              [2.1546050e-04, 1.4760980e-04, 1.6061604e-05],
              [2.1067094e-04, 1.4497816e-04, 1.9443167e-05],
              [2.0641519e-04, 1.3815981e-04, 2.6482619e-05],
              [2.0400202e-04, 1.3395854e-04, 3.0892639e-05],
              [1.9902988e-04, 1.3089213e-04, 3.5797952e-05],
              [1.9289140e-04, 1.2865067e-04, 3.6081707e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002396 0.0002716 ... 1.035e-05

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      array([[[2.39610745e-04, 2.71556084e-04, 2.70128832e-04],
              [2.40454014e-04, 2.70599645e-04, 2.74386490e-04],
              [2.38844208e-04, 2.72287260e-04, 2.75661034e-04],
              [2.39268178e-04, 2.72357342e-04, 2.73914455e-04],
              [2.38136767e-04, 2.70218472e-04, 2.74914317e-04],
              [2.40638648e-04, 2.65842449e-04, 2.78192281e-04],
              [2.39180474e-04, 2.64761271e-04, 2.83272122e-04],
              [2.38557084e-04, 2.67403608e-04, 2.80232227e-04],
              [2.40423135e-04, 2.67150201e-04, 2.80136708e-04],
              [2.40707173e-04, 2.69239477e-04, 2.72929028e-04],
              [2.40857858e-04, 2.67764030e-04, 2.78347288e-04],
              [2.40084250e-04, 2.69025011e-04, 2.78279331e-04],
              [2.41775560e-04, 2.68885138e-04, 2.73582235e-04],
              [2.38815803e-04, 2.68640055e-04, 2.75888189e-04],
              [2.39419605e-04, 2.66721123e-04, 2.76292703e-04],
              [2.39614528e-04, 2.65526905e-04, 2.78615538e-04],
              [2.39612215e-04, 2.63208203e-04, 2.79091299e-04],
              [2.38741486e-04, 2.62423680e-04, 2.80824257e-04],
              [2.38855631e-04, 2.61507288e-04, 2.79753702e-04],
              [2.38068678e-04, 2.64408678e-04, 2.79467757e-04],
      ...
              [4.28655549e-05, 3.03763100e-05, 1.97062764e-05],
              [4.30601140e-05, 3.16175210e-05, 1.90241481e-05],
              [4.39168252e-05, 3.30525654e-05, 1.80639163e-05],
              [4.37198396e-05, 3.46756715e-05, 1.31571032e-05],
              [4.35817747e-05, 3.43492829e-05, 9.33161937e-06],
              [4.41295670e-05, 3.12332850e-05, 7.67203892e-06],
              [4.47235834e-05, 2.71723238e-05, 6.19583034e-06],
              [4.54743567e-05, 2.19780250e-05, 5.31574733e-06],
              [4.57708011e-05, 1.73569697e-05, 4.65225321e-06],
              [4.60082410e-05, 1.53941182e-05, 4.28469411e-06],
              [4.54157052e-05, 1.37252200e-05, 4.09263794e-06],
              [4.43821191e-05, 1.29597693e-05, 4.18236550e-06],
              [4.52992681e-05, 1.17703157e-05, 3.95249845e-06],
              [4.71115054e-05, 1.77631937e-05, 3.62751052e-06],
              [4.60290321e-05, 2.39401288e-05, 4.93487551e-06],
              [4.58545837e-05, 2.56217081e-05, 5.63887988e-06],
              [4.60243464e-05, 2.62969115e-05, 7.22560526e-06],
              [4.58321811e-05, 2.66395800e-05, 8.58258500e-06],
              [4.60509145e-05, 2.69951252e-05, 9.79544802e-06],
              [4.51421438e-05, 2.74656868e-05, 1.03494922e-05]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      3.263 1.133 ... 0.2238 0.2969

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      array([[[3.2628639 , 1.132586  , 0.73984253],
              [3.2539244 , 1.1218415 , 0.6636562 ],
              [3.3077629 , 1.105064  , 0.657457  ],
              [3.199039  , 1.1237029 , 0.6516208 ],
              [3.407606  , 1.1350415 , 0.6307573 ],
              [3.551251  , 1.1491935 , 0.61930174],
              [3.7079988 , 1.1682833 , 0.6382069 ],
              [3.6843374 , 1.1969991 , 0.59546936],
              [3.6373034 , 1.1951932 , 0.58847064],
              [3.5286233 , 1.1990137 , 0.58950627],
              [3.5112283 , 1.2090486 , 0.5561185 ],
              [3.5832782 , 1.1928139 , 0.55407566],
              [3.6162767 , 1.197721  , 0.57535815],
              [3.6732585 , 1.2360073 , 0.6680939 ],
              [3.7434201 , 1.285768  , 0.6698258 ],
              [3.9210746 , 1.3179896 , 0.7200737 ],
              [4.1534557 , 1.3624804 , 0.72308815],
              [4.350609  , 1.3700106 , 0.72829914],
              [4.34737   , 1.4054053 , 0.74411196],
              [4.3841596 , 1.3435694 , 0.69021   ],
      ...
              [0.24822167, 0.2229091 , 0.22997776],
              [0.2502671 , 0.21606663, 0.2836377 ],
              [0.25094187, 0.20619413, 0.33462837],
              [0.25031978, 0.20203003, 0.39669967],
              [0.247252  , 0.20765834, 0.42320406],
              [0.24486798, 0.21742085, 0.43833   ],
              [0.2399987 , 0.22879753, 0.45103633],
              [0.2378625 , 0.24977483, 0.47837028],
              [0.23650162, 0.26490203, 0.50369376],
              [0.23604476, 0.27550876, 0.52109915],
              [0.2333923 , 0.27310133, 0.5113795 ],
              [0.23511393, 0.2857891 , 0.5116854 ],
              [0.19609238, 0.18043011, 0.53184634],
              [0.2021423 , 0.17112611, 0.33909   ],
              [0.20476255, 0.18330167, 0.29207003],
              [0.21014665, 0.19243236, 0.28591996],
              [0.21374545, 0.20258361, 0.2771893 ],
              [0.21875525, 0.21056795, 0.28172302],
              [0.2241939 , 0.21769346, 0.2848692 ],
              [0.23144257, 0.22380565, 0.29694226]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      3.261 1.081 0.6602 ... 0.1893 0.283

      long_name :

      $Ri^g_T$

      array([[[3.2613535 , 1.0807239 , 0.6601844 ],
              [3.2607088 , 1.0806876 , 0.6376095 ],
              [3.2541394 , 1.0787339 , 0.62403697],
              [3.1573467 , 1.095553  , 0.6195335 ],
              [3.33064   , 1.1045685 , 0.60020334],
              [3.4112148 , 1.1177173 , 0.57872057],
              [3.598929  , 1.1484542 , 0.59502447],
              [3.4866464 , 1.1638496 , 0.5739601 ],
              [3.478894  , 1.173312  , 0.5573975 ],
              [3.3537366 , 1.1519613 , 0.5667412 ],
              [3.2960985 , 1.1518707 , 0.5466292 ],
              [3.4085515 , 1.1364806 , 0.541849  ],
              [3.360477  , 1.1453516 , 0.56916815],
              [3.5060325 , 1.147816  , 0.64065945],
              [3.6237063 , 1.215729  , 0.6510779 ],
              [3.7974987 , 1.2563922 , 0.6879902 ],
              [4.037156  , 1.298629  , 0.6786159 ],
              [4.2002525 , 1.3074436 , 0.68557954],
              [4.2329836 , 1.3236547 , 0.71324885],
              [4.2080054 , 1.3151736 , 0.6461337 ],
      ...
              [0.19615808, 0.17713511, 0.17934614],
              [0.19791266, 0.16535255, 0.1786004 ],
              [0.19866161, 0.15758038, 0.18220627],
              [0.19679716, 0.14933537, 0.21518406],
              [0.19358617, 0.1441788 , 0.2393373 ],
              [0.18668334, 0.14489259, 0.255283  ],
              [0.18088251, 0.14877748, 0.27127427],
              [0.17411648, 0.15260799, 0.2903708 ],
              [0.16779342, 0.16068009, 0.30303138],
              [0.16678639, 0.16395637, 0.3173359 ],
              [0.16421464, 0.16863132, 0.3253452 ],
              [0.16048996, 0.17333059, 0.32974294],
              [0.15450153, 0.14349043, 0.34493858],
              [0.15779564, 0.14688559, 0.29000515],
              [0.1605247 , 0.1545775 , 0.25673643],
              [0.16559987, 0.16310802, 0.27112243],
              [0.17053735, 0.17055264, 0.2761741 ],
              [0.17479429, 0.1770179 , 0.27965385],
              [0.18131071, 0.18408577, 0.27241796],
              [0.18530713, 0.18929055, 0.2829873 ]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.02975 0.05414 ... 0.05437 0.08353

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.02975217, 0.05414128, 0.08337677],
             [0.02997051, 0.05396286, 0.08334547],
             [0.02989443, 0.05379027, 0.08344196],
             [0.02965429, 0.05390463, 0.08429465],
             [0.02966079, 0.05406607, 0.08407371],
             [0.02973503, 0.05454572, 0.08423293],
             [0.02972214, 0.05489863, 0.08463559],
             [0.02960968, 0.05482671, 0.08451898],
             [0.02952631, 0.05462506, 0.08395986],
             [0.02949316, 0.05446932, 0.08439989],
             [0.02946503, 0.05439705, 0.08432356],
             [0.0292913 , 0.05438457, 0.08425272],
             [0.02905069, 0.05423319, 0.08445528],
             [0.02918139, 0.05503562, 0.08494085],
             [0.02949452, 0.0555568 , 0.08575647],
             [0.02982584, 0.05614248, 0.08632967],
             [0.03012207, 0.05678497, 0.08614163],
             [0.0303158 , 0.05744474, 0.08672108],
             [0.03049818, 0.05776399, 0.08675309],
             [0.03012105, 0.05725272, 0.08582553],
             [0.03001658, 0.05665065, 0.08521383],
             [0.03018327, 0.05611375, 0.08581671],
             [0.02999875, 0.05514082, 0.08425651],
             [0.02983666, 0.05437292, 0.08352954]], dtype=float32)

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 1.001e-06
      eps       (depth, hour, tau_bins) float32 5.735e-10 5.908e-08 ... 1.889e-09
      chi       (depth, hour, tau_bins) float32 2.421e-08 4.218e-08 ... 1.773e-11
      Jb        (depth, hour, tau_bins) float32 -3.165e-10 ... -5.549e-12
      Jq        (depth, hour, tau_bins) float64 -0.4816 -0.6392 ... -0.01328
      S2        (depth, hour, tau_bins) float32 0.0001418 0.0004672 ... 1.13e-05
      N2        (depth, hour, tau_bins) float32 0.0002504 0.0002843 ... 7.772e-06
      Rig       (depth, hour, tau_bins) float32 1.384 0.4356 ... 0.3581 0.6224
      Rig_T     (depth, hour, tau_bins) float32 1.343 0.4258 ... 0.297 0.5564
      tau       (hour, tau_bins) float32 0.02927 0.0545 ... 0.05481 0.08379
  Attributes:
      title:    KPP ν0=2.5, Ri0=0.5

  new_baseline.kpp.lmd.005

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 1.001e-06 ... 1.001e-06

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062596e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062596e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062596e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062630e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062618e-06],
      ...
              [3.81811027e-04, 2.90885218e-04, 1.71408691e-02],
              [4.25665407e-04, 6.40261802e-04, 4.36169878e-02],
              [4.42612451e-04, 1.11911795e-03, 5.60436398e-02],
              [4.89161233e-04, 5.52093564e-03, 7.04844594e-02],
              [5.36391861e-04, 1.31646777e-02, 7.70594999e-02],
              [6.37809746e-04, 2.09450833e-02, 8.12212825e-02],
              [7.49726663e-04, 2.86364648e-02, 8.68151113e-02],
              [9.01583699e-04, 3.39822620e-02, 8.97497833e-02],
              [1.04411470e-03, 3.73836383e-02, 9.08122957e-02],
              [1.13334751e-03, 3.80024388e-02, 8.79565999e-02],
              [1.30286883e-03, 4.09585051e-02, 8.61921236e-02],
              [1.38791348e-03, 4.12570350e-02, 8.57141986e-02],
              [8.68165516e-04, 1.87469891e-03, 2.38270964e-02],
              [7.35677429e-04, 2.46586249e-04, 1.61692663e-03],
              [8.00757844e-04, 9.72932903e-05, 2.39668181e-03],
              [7.69015285e-04, 3.98732591e-05, 6.91609748e-04],
              [7.04221777e-04, 2.61289642e-05, 3.45671833e-05],
              [6.36181561e-04, 8.17534328e-06, 1.58894795e-06],
              [5.70160919e-04, 3.65733058e-06, 1.00068951e-06],
              [5.23375580e-04, 1.35793562e-06, 1.00068837e-06]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      5.735e-10 5.908e-08 ... 1.889e-09

      long_name :

      $SP$

      units :

      W/kg

      array([[[5.73483705e-10, 5.90838596e-08, 2.15448935e-07],
              [5.81091175e-10, 5.87181788e-08, 2.12549949e-07],
              [6.81909473e-10, 5.76417030e-08, 2.00810078e-07],
              [6.64125366e-10, 5.87719846e-08, 1.89282304e-07],
              [5.68308345e-10, 5.55936523e-08, 1.68704887e-07],
              [4.92745234e-10, 4.92134227e-08, 1.76220226e-07],
              [4.96048647e-10, 4.67140282e-08, 1.57617066e-07],
              [5.01904074e-10, 4.51418529e-08, 1.46062305e-07],
              [5.14162712e-10, 4.29492850e-08, 1.45271159e-07],
              [5.65038460e-10, 4.10675156e-08, 1.46149574e-07],
              [5.86884707e-10, 4.17339230e-08, 1.49086731e-07],
              [6.35932196e-10, 4.17201989e-08, 1.53073813e-07],
              [6.37232100e-10, 4.25185576e-08, 1.53210635e-07],
              [6.10381190e-10, 3.85168306e-08, 1.55136433e-07],
              [5.65663461e-10, 3.38582353e-08, 1.52575666e-07],
              [5.15196330e-10, 3.19983080e-08, 1.39071460e-07],
              [4.52812149e-10, 2.97344300e-08, 1.87967046e-07],
              [4.13996726e-10, 2.86390218e-08, 1.96979386e-07],
              [3.91505439e-10, 2.65191584e-08, 1.93616984e-07],
              [3.97265304e-10, 3.43650264e-08, 2.04911160e-07],
      ...
              [5.93399214e-08, 6.42338165e-08, 2.96590201e-07],
              [6.45967404e-08, 1.58682056e-07, 3.79704971e-07],
              [7.07415992e-08, 2.60141974e-07, 3.58420380e-07],
              [8.38285246e-08, 3.46018254e-07, 2.93743597e-07],
              [1.02582291e-07, 3.65818039e-07, 2.58425359e-07],
              [1.33359805e-07, 3.55552288e-07, 2.17777028e-07],
              [1.73986905e-07, 3.15978582e-07, 1.88055054e-07],
              [2.09051464e-07, 2.73394363e-07, 1.64039164e-07],
              [2.38001761e-07, 2.25841461e-07, 1.45215409e-07],
              [2.31611693e-07, 2.02772654e-07, 1.26571138e-07],
              [2.34431269e-07, 1.88707361e-07, 1.14071625e-07],
              [2.39257758e-07, 1.72018076e-07, 1.11745550e-07],
              [1.18438180e-07, 4.03291729e-08, 4.43827801e-08],
              [1.16053108e-07, 1.05902611e-08, 8.01906808e-09],
              [1.20561225e-07, 1.02162936e-08, 1.02953468e-08],
              [1.12213129e-07, 9.34675537e-09, 5.69340086e-09],
              [1.01487046e-07, 7.98773758e-09, 2.47878096e-09],
              [8.98038195e-08, 7.39516626e-09, 1.49280566e-09],
              [8.13782322e-08, 7.12342629e-09, 1.23643273e-09],
              [7.33811589e-08, 7.11201409e-09, 1.88859817e-09]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      2.421e-08 4.218e-08 ... 1.773e-11

      long_name :

      $χ$

      units :

      C^2/s

      array([[[2.42052565e-08, 4.21824211e-08, 6.13486506e-08],
              [2.43459812e-08, 4.24095639e-08, 6.18638438e-08],
              [2.49258587e-08, 4.28722551e-08, 6.38544719e-08],
              [2.41345166e-08, 4.36538734e-08, 6.12625897e-08],
              [2.41304896e-08, 4.32199982e-08, 6.58363177e-08],
              [2.39560300e-08, 4.23458459e-08, 6.48738592e-08],
              [2.39092675e-08, 4.18693453e-08, 6.48294289e-08],
              [2.36714595e-08, 4.21219220e-08, 6.36250235e-08],
              [2.35294735e-08, 4.19933741e-08, 6.35801527e-08],
              [2.38001778e-08, 4.14795842e-08, 6.10450712e-08],
              [2.42841285e-08, 4.09588949e-08, 6.14152711e-08],
              [2.45673526e-08, 4.12250003e-08, 6.06932744e-08],
              [2.50661039e-08, 4.04394562e-08, 6.33500861e-08],
              [2.45900935e-08, 4.08110026e-08, 6.14272153e-08],
              [2.40473650e-08, 3.92472401e-08, 6.08052844e-08],
              [2.33138522e-08, 3.92737824e-08, 5.67299274e-08],
              [2.31825688e-08, 3.83459664e-08, 5.78424597e-08],
              [2.29814709e-08, 3.78962568e-08, 5.81310964e-08],
              [2.24128005e-08, 3.67799586e-08, 5.87707021e-08],
              [2.26952821e-08, 3.83477641e-08, 5.96626819e-08],
      ...
              [6.58048833e-08, 3.45897888e-08, 3.06971515e-07],
              [7.74475311e-08, 1.30058410e-07, 4.16388218e-07],
              [8.59298765e-08, 2.74952754e-07, 3.99758875e-07],
              [9.98236942e-08, 3.31715938e-07, 2.91729918e-07],
              [1.24177078e-07, 3.48626457e-07, 2.22114693e-07],
              [1.59281512e-07, 3.29062061e-07, 1.67315164e-07],
              [1.86461662e-07, 2.82267621e-07, 1.27866073e-07],
              [2.02538843e-07, 2.26290396e-07, 9.58634558e-08],
              [2.10553225e-07, 1.79261093e-07, 8.23487909e-08],
              [1.90766357e-07, 1.44952992e-07, 7.88916452e-08],
              [1.84443806e-07, 1.37191520e-07, 7.31033865e-08],
              [1.62389298e-07, 1.18587856e-07, 7.63602017e-08],
              [8.45517505e-08, 9.69671721e-09, 2.11790869e-08],
              [7.38182564e-08, 1.38504741e-09, 1.56564184e-09],
              [1.16998599e-07, 9.71107417e-10, 3.63399155e-09],
              [1.27962963e-07, 5.68661396e-10, 2.04201012e-09],
              [1.21315836e-07, 4.69920158e-10, 1.60213856e-10],
              [1.14172195e-07, 1.94918595e-10, 2.22373005e-11],
              [1.04255875e-07, 1.51198110e-10, 1.14214176e-11],
              [9.37985618e-08, 1.06092787e-10, 1.77340642e-11]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      -3.165e-10 ... -5.549e-12

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[-3.16469878e-10, -4.06335215e-10, -4.95005370e-10],
              [-3.17809973e-10, -4.08817480e-10, -5.05972153e-10],
              [-3.19277160e-10, -4.06790518e-10, -5.20859522e-10],
              [-3.16120546e-10, -4.13883317e-10, -5.00933961e-10],
              [-3.13139681e-10, -4.10488865e-10, -5.20764376e-10],
              [-3.10633408e-10, -4.08861972e-10, -5.17567100e-10],
              [-3.07887188e-10, -4.05906087e-10, -5.04892905e-10],
              [-3.05545256e-10, -4.04478839e-10, -5.07665687e-10],
              [-3.04483549e-10, -4.05523504e-10, -5.02378528e-10],
              [-3.06875358e-10, -4.05313921e-10, -4.88781682e-10],
              [-3.09053727e-10, -4.06532308e-10, -4.90021246e-10],
              [-3.14069798e-10, -4.04227846e-10, -4.88690088e-10],
              [-3.19417853e-10, -4.00812328e-10, -4.95343877e-10],
              [-3.16587395e-10, -4.00275202e-10, -4.89477736e-10],
              [-3.14177545e-10, -3.95527555e-10, -4.95618879e-10],
              [-3.08587988e-10, -3.92145816e-10, -4.73352690e-10],
              [-3.05383857e-10, -3.87099630e-10, -4.77537287e-10],
              [-3.04662073e-10, -3.82146120e-10, -4.79010498e-10],
              [-3.03895215e-10, -3.79181186e-10, -4.81312434e-10],
              [-3.04242853e-10, -3.84696941e-10, -4.91413632e-10],
      ...
              [-5.13596010e-09, -1.12433076e-08, -1.73453998e-07],
              [-6.16254914e-09, -2.75167942e-08, -1.78586021e-07],
              [-7.04082215e-09, -5.64420901e-08, -1.63006959e-07],
              [-9.40332612e-09, -8.36426182e-08, -1.39608161e-07],
              [-1.21739756e-08, -9.57661683e-08, -1.16583863e-07],
              [-1.44949128e-08, -9.81782833e-08, -1.04734198e-07],
              [-1.82505584e-08, -9.62569473e-08, -9.39466602e-08],
              [-2.09147721e-08, -8.83932714e-08, -8.06017368e-08],
              [-2.07831228e-08, -7.65039445e-08, -8.95265799e-08],
              [-2.29593340e-08, -7.25662659e-08, -7.88470302e-08],
              [-2.28492016e-08, -6.55638672e-08, -8.51448192e-08],
              [-7.32230188e-09, -3.86669319e-09, -2.06859028e-08],
              [-5.07420683e-09, -1.86237886e-10, -6.27351116e-10],
              [-8.44772785e-09, -7.74282166e-11, -2.03356487e-09],
              [-9.28062427e-09, -1.02767976e-11, -6.27712660e-10],
              [-8.80577300e-09, -6.66913183e-12, -3.52873321e-11],
              [-8.48780868e-09, -5.79612356e-12, -6.17024704e-12],
              [-7.54977414e-09, -5.89862187e-12, -4.79358827e-12],
              [-6.58689014e-09, -6.02329558e-12, -5.54850436e-12]]],
            dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.4816 -0.6392 ... -0.01328

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[-4.81552918e-01, -6.39167771e-01, -7.62933746e-01],
              [-4.85329101e-01, -6.42421775e-01, -7.69579102e-01],
              [-4.90946456e-01, -6.40105755e-01, -7.79354780e-01],
              [-4.83578128e-01, -6.47227242e-01, -7.73838051e-01],
              [-4.83167888e-01, -6.42581872e-01, -7.91665006e-01],
              [-4.80104343e-01, -6.40745775e-01, -7.90478163e-01],
              [-4.77434282e-01, -6.38051315e-01, -7.81818421e-01],
              [-4.75500770e-01, -6.35332332e-01, -7.90931913e-01],
              [-4.73519131e-01, -6.37496725e-01, -7.95123686e-01],
              [-4.78122582e-01, -6.36972344e-01, -7.80516887e-01],
              [-4.82024922e-01, -6.37689752e-01, -7.90518737e-01],
              [-4.84282250e-01, -6.35676375e-01, -7.79288919e-01],
              [-4.88477602e-01, -6.29759740e-01, -7.87195326e-01],
              [-4.83797879e-01, -6.26795017e-01, -7.73909660e-01],
              [-4.78714276e-01, -6.17771603e-01, -7.74951022e-01],
              [-4.71088650e-01, -6.12925103e-01, -7.50714301e-01],
              [-4.70555356e-01, -6.07242223e-01, -7.45696696e-01],
              [-4.70414981e-01, -6.04348376e-01, -7.45931460e-01],
              [-4.67814145e-01, -5.97328799e-01, -7.44359541e-01],
              [-4.64213041e-01, -6.07979658e-01, -7.65086306e-01],
      ...
              [-1.52504881e+01, -1.08198466e+01, -2.40912335e+02],
              [-1.79780728e+01, -2.77116033e+01, -4.22594618e+02],
              [-1.87859441e+01, -6.95042618e+01, -4.35070729e+02],
              [-2.05550840e+01, -1.68302990e+02, -4.19219828e+02],
              [-2.43782413e+01, -2.28539469e+02, -3.79352246e+02],
              [-2.99324167e+01, -2.53416225e+02, -3.39421920e+02],
              [-3.62329256e+01, -2.54032476e+02, -3.03281977e+02],
              [-4.57524427e+01, -2.37408954e+02, -2.80372229e+02],
              [-5.52564399e+01, -2.23234615e+02, -2.56960106e+02],
              [-5.82335571e+01, -2.00110950e+02, -2.44827332e+02],
              [-6.62942474e+01, -1.96551320e+02, -2.38200016e+02],
              [-7.06634387e+01, -1.84589225e+02, -2.36158794e+02],
              [-2.57588716e+01, -1.98516862e+01, -7.16579682e+01],
              [-2.11297949e+01, -2.07265483e+00, -6.26872271e+00],
              [-2.92281227e+01, -1.13948511e+00, -1.02573476e+01],
              [-2.99932999e+01, -5.12616250e-01, -4.02535216e+00],
              [-2.89512937e+01, -3.15996434e-01, -2.26048104e-01],
              [-2.69464025e+01, -1.24594175e-01, -1.93938576e-02],
              [-2.45196400e+01, -7.30878555e-02, -1.04230675e-02],
              [-2.18747312e+01, -4.18680368e-02, -1.32777846e-02]]])

    • S2
      (depth, hour, tau_bins)
      float32
      0.0001418 0.0004672 ... 1.13e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[1.41836601e-04, 4.67205304e-04, 7.15640199e-04],
              [1.41885423e-04, 4.72498999e-04, 7.31437409e-04],
              [1.48016523e-04, 4.70162660e-04, 7.41417753e-04],
              [1.40774020e-04, 4.72693850e-04, 7.54935376e-04],
              [1.37023657e-04, 4.75256471e-04, 6.97829120e-04],
              [1.28246960e-04, 4.59599047e-04, 7.07112486e-04],
              [1.25261853e-04, 4.47610510e-04, 6.60455436e-04],
              [1.26983170e-04, 4.52060514e-04, 6.61135535e-04],
              [1.30664936e-04, 4.44407866e-04, 6.85080537e-04],
              [1.37154537e-04, 4.38653689e-04, 6.94098591e-04],
              [1.42129575e-04, 4.40310774e-04, 7.55701447e-04],
              [1.44565871e-04, 4.36307222e-04, 7.79487425e-04],
              [1.45691098e-04, 4.47019353e-04, 7.87896803e-04],
              [1.44280799e-04, 4.34455666e-04, 7.75266206e-04],
              [1.38319811e-04, 4.21505451e-04, 7.35988317e-04],
              [1.30991262e-04, 4.10690991e-04, 7.31264823e-04],
              [1.23740640e-04, 3.95052601e-04, 7.36095477e-04],
              [1.19035845e-04, 3.91300389e-04, 7.24209938e-04],
              [1.12783542e-04, 3.89472814e-04, 7.19981967e-04],
              [1.15830204e-04, 4.13761125e-04, 7.08727632e-04],
      ...
              [1.48438034e-04, 8.40546709e-05, 3.76138487e-05],
              [1.49744868e-04, 8.34067832e-05, 2.62681206e-05],
              [1.55048183e-04, 8.11126229e-05, 1.71083029e-05],
              [1.57887640e-04, 7.14546768e-05, 1.13211072e-05],
              [1.63779288e-04, 5.51105259e-05, 8.39280438e-06],
              [1.68770872e-04, 4.05372193e-05, 6.78651486e-06],
              [1.68515748e-04, 2.62506983e-05, 5.61330125e-06],
              [1.69917534e-04, 1.88715403e-05, 4.70684881e-06],
              [1.62872253e-04, 1.40121865e-05, 4.11091787e-06],
              [1.63078847e-04, 1.16760530e-05, 3.64465927e-06],
              [1.55001995e-04, 1.02589747e-05, 3.68917995e-06],
              [1.50725929e-04, 9.34424224e-06, 3.74268780e-06],
              [1.62076351e-04, 1.31826091e-05, 3.24889447e-06],
              [1.72409025e-04, 1.79971466e-05, 4.08658934e-06],
              [1.71353196e-04, 2.51919682e-05, 4.80949393e-06],
              [1.65656005e-04, 3.08949420e-05, 5.40391466e-06],
              [1.60071344e-04, 3.37284146e-05, 6.34383969e-06],
              [1.54592650e-04, 3.72321556e-05, 7.46843898e-06],
              [1.50900625e-04, 4.07821572e-05, 8.78149694e-06],
              [1.46861552e-04, 4.46950471e-05, 1.12951284e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002504 0.0002843 ... 7.772e-06

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      array([[[2.50380457e-04, 2.84322537e-04, 3.26497160e-04],
              [2.49794335e-04, 2.84366863e-04, 3.40093946e-04],
              [2.50732730e-04, 2.82385619e-04, 3.52266827e-04],
              [2.48224998e-04, 2.84529960e-04, 3.56787234e-04],
              [2.50212091e-04, 2.83362053e-04, 3.48632922e-04],
              [2.50026409e-04, 2.83005007e-04, 3.47889756e-04],
              [2.52470782e-04, 2.82140682e-04, 3.33684904e-04],
              [2.52225524e-04, 2.83426692e-04, 3.28114023e-04],
              [2.52644822e-04, 2.83705245e-04, 3.10908479e-04],
              [2.55907566e-04, 2.84160778e-04, 2.99137144e-04],
              [2.56487081e-04, 2.83852918e-04, 2.97512830e-04],
              [2.59825640e-04, 2.79677886e-04, 3.08105693e-04],
              [2.58581596e-04, 2.79740925e-04, 3.08726507e-04],
              [2.58143933e-04, 2.80908833e-04, 3.07591661e-04],
              [2.53710255e-04, 2.79614876e-04, 3.14174657e-04],
              [2.53522012e-04, 2.79249914e-04, 3.10601346e-04],
              [2.52498110e-04, 2.75294151e-04, 3.15695826e-04],
              [2.51230667e-04, 2.75489874e-04, 3.10448901e-04],
              [2.47999094e-04, 2.76833016e-04, 3.11023148e-04],
              [2.47006770e-04, 2.79002707e-04, 3.11012758e-04],
      ...
              [4.00318095e-05, 2.19094200e-05, 1.75809691e-05],
              [4.08545384e-05, 2.29993839e-05, 1.44682808e-05],
              [4.17602168e-05, 2.32393431e-05, 1.14376789e-05],
              [4.19672360e-05, 2.17431643e-05, 7.65365985e-06],
              [4.25652397e-05, 1.95154098e-05, 5.98406086e-06],
              [4.24695463e-05, 1.64385110e-05, 5.06878587e-06],
              [4.20580618e-05, 1.29026885e-05, 4.27718123e-06],
              [4.17472147e-05, 9.78873140e-06, 3.76438538e-06],
              [4.05107130e-05, 7.85583507e-06, 3.40888255e-06],
              [3.98970660e-05, 6.68765961e-06, 3.17039030e-06],
              [3.72795621e-05, 6.00186786e-06, 3.05350545e-06],
              [3.63023901e-05, 5.69715758e-06, 3.10032669e-06],
              [3.58221514e-05, 5.26593840e-06, 2.91269725e-06],
              [3.86316751e-05, 5.47839863e-06, 2.88081537e-06],
              [4.08812521e-05, 7.10814311e-06, 3.56441478e-06],
              [4.26108927e-05, 8.30356112e-06, 4.09821496e-06],
              [4.13776215e-05, 9.73325223e-06, 4.94596861e-06],
              [4.03243102e-05, 1.09723651e-05, 5.75752665e-06],
              [4.00129029e-05, 1.23583932e-05, 6.63946867e-06],
              [3.99043274e-05, 1.35408591e-05, 7.77176410e-06]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      1.384 0.4356 ... 0.3581 0.6224

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      array([[[1.3843784 , 0.4355776 , 0.3091246 ],
              [1.3862953 , 0.42921245, 0.29979953],
              [1.3186188 , 0.43328127, 0.29791135],
              [1.319762  , 0.431867  , 0.31552285],
              [1.3876023 , 0.42956513, 0.33238146],
              [1.4713855 , 0.43854415, 0.33611113],
              [1.4585589 , 0.44340074, 0.3397169 ],
              [1.422827  , 0.44158596, 0.33740765],
              [1.4308827 , 0.44296315, 0.3283518 ],
              [1.3624867 , 0.44709092, 0.32959506],
              [1.3321421 , 0.44995126, 0.31895676],
              [1.3095046 , 0.44489077, 0.31756058],
              [1.299477  , 0.44661266, 0.31964993],
              [1.3353751 , 0.45561314, 0.32711515],
              [1.3878534 , 0.46209648, 0.33242503],
              [1.4295163 , 0.47146723, 0.3417432 ],
              [1.5292119 , 0.48259148, 0.33962864],
              [1.5888655 , 0.49773607, 0.3420213 ],
              [1.6786402 , 0.50815296, 0.34517553],
              [1.6653159 , 0.49143514, 0.32707235],
      ...
              [0.28946295, 0.31139803, 0.48474592],
              [0.28830624, 0.31103155, 0.6091647 ],
              [0.28619725, 0.31105167, 0.6668595 ],
              [0.27883843, 0.3508357 , 0.70064497],
              [0.27638355, 0.40184402, 0.73615754],
              [0.2694257 , 0.4452605 , 0.747144  ],
              [0.2700301 , 0.5024443 , 0.76513684],
              [0.27028793, 0.53571266, 0.8011063 ],
              [0.27429098, 0.5733974 , 0.8354088 ],
              [0.27542618, 0.5895082 , 0.85250646],
              [0.27886292, 0.59905994, 0.8383981 ],
              [0.27949739, 0.61504954, 0.85223484],
              [0.23919442, 0.4293044 , 0.8984655 ],
              [0.23977003, 0.3274899 , 0.7073171 ],
              [0.24828433, 0.31492984, 0.70929885],
              [0.2581412 , 0.31994987, 0.7032059 ],
              [0.2622922 , 0.32738152, 0.69430584],
              [0.26598185, 0.33688056, 0.6817956 ],
              [0.27302545, 0.34765816, 0.68264323],
              [0.28132206, 0.35813454, 0.6223506 ]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      1.343 0.4258 0.304 ... 0.297 0.5564

      long_name :

      $Ri^g_T$

      array([[[1.3434284 , 0.42575198, 0.30399483],
              [1.339348  , 0.42032528, 0.2998174 ],
              [1.2688062 , 0.42568558, 0.3012114 ],
              [1.2518506 , 0.42318174, 0.3039454 ],
              [1.3326304 , 0.42496133, 0.3102635 ],
              [1.4218832 , 0.43632212, 0.31440943],
              [1.384247  , 0.44214174, 0.33351892],
              [1.3839062 , 0.4455327 , 0.32989636],
              [1.3978841 , 0.4521952 , 0.32683736],
              [1.3210678 , 0.4536286 , 0.31877822],
              [1.2479538 , 0.4600122 , 0.3171023 ],
              [1.2329819 , 0.45810977, 0.31853944],
              [1.244715  , 0.45624778, 0.31939033],
              [1.2971063 , 0.46135846, 0.32418156],
              [1.3399007 , 0.46895933, 0.33118114],
              [1.3868494 , 0.47377062, 0.34303665],
              [1.536852  , 0.4890711 , 0.33796883],
              [1.5975964 , 0.49740615, 0.33952767],
              [1.6472256 , 0.50301504, 0.33876863],
              [1.6406693 , 0.49364513, 0.3232821 ],
      ...
              [0.21892303, 0.24460581, 0.2782022 ],
              [0.21715575, 0.23441526, 0.3032993 ],
              [0.21339254, 0.22937962, 0.33986917],
              [0.20731284, 0.22571316, 0.3779726 ],
              [0.20430309, 0.23617734, 0.4060605 ],
              [0.19837219, 0.24865082, 0.4239027 ],
              [0.19388697, 0.27108073, 0.44667554],
              [0.19081205, 0.2972318 , 0.46966553],
              [0.18402362, 0.32012552, 0.47574738],
              [0.18582553, 0.3224433 , 0.5086578 ],
              [0.18361391, 0.34084806, 0.52148837],
              [0.18255128, 0.34092087, 0.52430046],
              [0.17780563, 0.27581286, 0.59936225],
              [0.18213266, 0.26158136, 0.53423965],
              [0.18586144, 0.25454444, 0.54415107],
              [0.19341025, 0.26411104, 0.5709722 ],
              [0.19832414, 0.2755742 , 0.5958633 ],
              [0.20684622, 0.28239402, 0.59805214],
              [0.21064335, 0.29060137, 0.5932599 ],
              [0.2162599 , 0.29699275, 0.5563707 ]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.02927 0.0545 ... 0.05481 0.08379

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.02926666, 0.05449591, 0.08384066],
             [0.02953365, 0.05412898, 0.08342438],
             [0.02950766, 0.0540043 , 0.08344106],
             [0.02929587, 0.05410497, 0.08466474],
             [0.0292785 , 0.05410444, 0.08424635],
             [0.02926119, 0.05435768, 0.08391857],
             [0.02946588, 0.05500093, 0.08486678],
             [0.02949753, 0.05470305, 0.08457853],
             [0.02931711, 0.05448961, 0.08386523],
             [0.02923332, 0.05441001, 0.08456027],
             [0.02923119, 0.05438141, 0.08475135],
             [0.02906845, 0.05451858, 0.0846531 ],
             [0.02867212, 0.05450471, 0.08504945],
             [0.02912105, 0.05511454, 0.0853009 ],
             [0.02948293, 0.05570985, 0.08595277],
             [0.0298175 , 0.05617661, 0.08633688],
             [0.030358  , 0.0568147 , 0.08630857],
             [0.0307796 , 0.05742799, 0.08671759],
             [0.03049245, 0.05769216, 0.08660597],
             [0.0303795 , 0.05715569, 0.08534982],
             [0.03001719, 0.05657351, 0.08543649],
             [0.03012108, 0.05630453, 0.08567342],
             [0.02991865, 0.05550613, 0.08454789],
             [0.02963275, 0.05481243, 0.08379018]], dtype=float32)

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ri0=0.5

• Dimensions:
• Coordinates: (0)
• Data variables: (0)
• Attributes: (0)

seasonal cycle

tree = mixpods.persist_tree(tree)

Validate before continuing

mixpods.validate_tree(tree)

TODO: Verify depth is normalized

for name, ds in datasets.items():
    (ds.cf["sea_water_x_velocity"].cf["Z"].plot(marker=".", ls="none", label=name))
plt.legend()

<matplotlib.legend.Legend>

Mean profiles

%autoreload

S2 = mixpods.plot_profile_fill(tree, "S2", "S^2")
N2 = mixpods.plot_profile_fill(tree, "N2T", "N_T^2")
u = mixpods.plot_profile_fill(tree, "sea_water_x_velocity", "u")
T = mixpods.plot_profile_fill(tree, "sea_water_potential_temperature", "T")
Ri = mixpods.plot_median_Ri(tree)

(S2 + N2 + Ri + u + T).cols(3)

Stability Diagram

%autoreload

mixpods.plot_stability_diagram_by_dataset(tree, nrows=2)

Daily composites

%autoreload

mixpods.plot_daily_composites(dailies, ["eps", "KT"], logy=True, legend=False).opts(
    hv.opts.GridSpace(show_legend=True)
)

%autoreload

mixpods.plot_daily_composites(
    dailies, ["S2", "N2", "Rig_T"], logy=False, legend=False
).opts(hv.opts.GridSpace(show_legend=True, width=200), hv.opts.Overlay(frame_width=200))

Turbulence distributions

handles = [
    mixpods.plot_distributions(tree, "chi", bins=np.linspace(-11, -4, 101), log=True),
    mixpods.plot_distributions(tree, "eps", bins=np.linspace(-11, -4, 101), log=True),
    mixpods.plot_distributions(
        tree, "ocean_vertical_heat_diffusivity", bins=np.linspace(-8, -1, 101), log=True
    ),
    # plot_distributions(tree, "Jq", bins=np.linspace(-1000, 0, 51), log=False),
    mixpods.plot_distributions(tree, "Rig_T", np.linspace(-0.5, 1.5, 61))
    * hv.VLine(0.25).opts(line_color="k"),
]

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:769: RuntimeWarning: invalid value encountered in log10
  result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:769: RuntimeWarning: invalid value encountered in log10
  result_data = func(*input_data)

hv.Layout(handles).opts("Overlay", frame_width=600).cols(2)

Compare boundary layer depth

hbl = (
    tree.drop_nodes("TAO")
    .dc.subset_nodes("KPP_OBLdepth")
    .dc.concatenate_nodes()
    .reset_coords(drop=True)
).load()

(
    # hbl.groupby("time.hour").mean().hvplot.line(by="node", flip_yaxis=True)
    hbl.groupby("time.hour").median().hvplot.line(by="node", flip_yaxis=True)
    + hbl.to_dataframe().hvplot.hist(
        by="node",
        bins=np.arange(0, 90, 1),
        normed=1,
        alpha=0.3,
        ylim=(0, 0.05),
        muted_alpha=0,
    )
).opts(hv.opts.Curve(invert_yaxis=True))